diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index e9930038..fb524f92 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -27,8 +27,12 @@ jobs:
           unzip -o benchmark_data.zip
       - name: Run benchmark test
         run: |
-          poetry run python benchmark.py benchmark_data/pdfs benchmark_data/references report.json
-          poetry run python scripts/verify_benchmark_scores.py report.json
+          poetry run python benchmarks/overall.py benchmark_data/pdfs benchmark_data/references report.json
+          poetry run python scripts/verify_benchmark_scores.py report.json --type marker
+      - name: Run table benchmark
+        run: |
+          poetry run python benchmarks/table.py tables.json
+          poetry run python scripts/verify_benchmark_scores.py tables.json --type table
         
           
 
diff --git a/README.md b/README.md
index 3f48bc58..a546b633 100644
--- a/README.md
+++ b/README.md
@@ -88,32 +88,40 @@ First, some configuration:
 
 - Inspect the settings in `marker/settings.py`.  You can override any settings with environment variables.
 - Your torch device will be automatically detected, but you can override this.  For example, `TORCH_DEVICE=cuda`.
-  - If using GPU, set `INFERENCE_RAM` to your GPU VRAM (per GPU).  For example, if you have 16 GB of VRAM, set `INFERENCE_RAM=16`.
-  - Depending on your document types, marker's average memory usage per task can vary slightly.  You can configure `VRAM_PER_TASK` to adjust this if you notice tasks failing with GPU out of memory errors.
-- By default, marker will use `surya` for OCR.  Surya is slower on CPU, but more accurate than tesseract.  If you want faster OCR, set `OCR_ENGINE` to `ocrmypdf`. This also requires external dependencies (see above).  If you don't want OCR at all, set `OCR_ENGINE` to `None`.
+- By default, marker will use `surya` for OCR.  Surya is slower on CPU, but more accurate than tesseract.  It also doesn't require you to specify the languages in the document.  If you want faster OCR, set `OCR_ENGINE` to `ocrmypdf`. This also requires external dependencies (see above).  If you don't want OCR at all, set `OCR_ENGINE` to `None`.
+
+## Interactive App
+
+I've included a streamlit app that lets you interactively try marker with some basic options.  Run it with:
+
+```shell
+pip install streamlit
+marker_gui
+```
 
 ## Convert a single file
 
 ```shell
-marker_single /path/to/file.pdf /path/to/output/folder --batch_multiplier 2 --max_pages 10 --langs English
+marker_single /path/to/file.pdf /path/to/output/folder --batch_multiplier 2 --max_pages 10 
 ```
 
 - `--batch_multiplier` is how much to multiply default batch sizes by if you have extra VRAM.  Higher numbers will take more VRAM, but process faster.  Set to 2 by default.  The default batch sizes will take ~3GB of VRAM.
 - `--max_pages` is the maximum number of pages to process.  Omit this to convert the entire document.
-- `--langs` is a comma separated list of the languages in the document, for OCR
+- `--langs` is an optional comma separated list of the languages in the document, for OCR.  Optional by default, required if you use tesseract.
+- `--ocr_all_pages` is an optional argument to force OCR on all pages of the PDF.  If this or the env var `OCR_ALL_PAGES` are true, OCR will be forced.
 
-Make sure the `DEFAULT_LANG` setting is set appropriately for your document.  The list of supported languages for OCR is [here](https://github.com/VikParuchuri/surya/blob/master/surya/languages.py).  If you need more languages, you can use any language supported by [Tesseract](https://tesseract-ocr.github.io/tessdoc/Data-Files#data-files-for-version-400-november-29-2016) if you set `OCR_ENGINE` to `ocrmypdf`.  If you don't need OCR, marker can work with any language.
+The list of supported languages for surya OCR is [here](https://github.com/VikParuchuri/surya/blob/master/surya/languages.py).  If you need more languages, you can use any language supported by [Tesseract](https://tesseract-ocr.github.io/tessdoc/Data-Files#data-files-for-version-400-november-29-2016) if you set `OCR_ENGINE` to `ocrmypdf`.  If you don't need OCR, marker can work with any language.
 
 ## Convert multiple files
 
 ```shell
-marker /path/to/input/folder /path/to/output/folder --workers 10 --max 10 --metadata_file /path/to/metadata.json --min_length 10000
+marker /path/to/input/folder /path/to/output/folder --workers 4 --max 10 --min_length 10000
 ```
 
-- `--workers` is the number of pdfs to convert at once.  This is set to 1 by default, but you can increase it to increase throughput, at the cost of more CPU/GPU usage. Parallelism will not increase beyond `INFERENCE_RAM / VRAM_PER_TASK` if you're using GPU.
+- `--workers` is the number of pdfs to convert at once.  This is set to 1 by default, but you can increase it to increase throughput, at the cost of more CPU/GPU usage.  Marker will use 5GB of VRAM per worker at the peak, and 3.5GB average.
 - `--max` is the maximum number of pdfs to convert.  Omit this to convert all pdfs in the folder.
 - `--min_length` is the minimum number of characters that need to be extracted from a pdf before it will be considered for processing.  If you're processing a lot of pdfs, I recommend setting this to avoid OCRing pdfs that are mostly images. (slows everything down)
-- `--metadata_file` is an optional path to a json file with metadata about the pdfs.  If you provide it, it will be used to set the language for each pdf.  If not, `DEFAULT_LANG` will be used. The format is:
+- `--metadata_file` is an optional path to a json file with metadata about the pdfs.  If you provide it, it will be used to set the language for each pdf.  Setting language is optional for surya (default), but required for tesseract. The format is:
 
 ```
 {
@@ -133,7 +141,7 @@ MIN_LENGTH=10000 METADATA_FILE=../pdf_meta.json NUM_DEVICES=4 NUM_WORKERS=15 mar
 
 - `METADATA_FILE` is an optional path to a json file with metadata about the pdfs.  See above for the format.
 - `NUM_DEVICES` is the number of GPUs to use.  Should be `2` or greater.
-- `NUM_WORKERS` is the number of parallel processes to run on each GPU.  Per-GPU parallelism will not increase beyond `INFERENCE_RAM / VRAM_PER_TASK`.
+- `NUM_WORKERS` is the number of parallel processes to run on each GPU.
 - `MIN_LENGTH` is the minimum number of characters that need to be extracted from a pdf before it will be considered for processing.  If you're processing a lot of pdfs, I recommend setting this to avoid OCRing pdfs that are mostly images. (slows everything down)
 
 Note that the env variables above are specific to this script, and cannot be set in `local.env`.
@@ -199,16 +207,24 @@ git clone https://github.com/VikParuchuri/marker.git
 poetry install
 ```
 
-Download the benchmark data [here](https://drive.google.com/file/d/1ZSeWDo2g1y0BRLT7KnbmytV2bjWARWba/view?usp=sharing) and unzip. Then run `benchmark.py` like this:
+Download the benchmark data [here](https://drive.google.com/file/d/1ZSeWDo2g1y0BRLT7KnbmytV2bjWARWba/view?usp=sharing) and unzip. Then run the overall benchmark like this:
 
 ```shell
-python benchmark.py data/pdfs data/references report.json --nougat
+python benchmark/overall.py data/pdfs data/references report.json --nougat
 ```
 
 This will benchmark marker against other text extraction methods.  It sets up batch sizes for nougat and marker to use a similar amount of GPU RAM for each.
 
 Omit `--nougat` to exclude nougat from the benchmark.  I don't recommend running nougat on CPU, since it is very slow.
 
+### Table benchmark
+
+There is a benchmark for table parsing, which you can run with:
+
+```shell
+python benchmarks/table.py test_data/tables.json
+```
+
 # Thanks
 
 This work would not have been possible without amazing open source models and datasets, including (but not limited to):
diff --git a/benchmark.py b/benchmarks/overall.py
similarity index 100%
rename from benchmark.py
rename to benchmarks/overall.py
diff --git a/benchmarks/table.py b/benchmarks/table.py
new file mode 100644
index 00000000..45cd3888
--- /dev/null
+++ b/benchmarks/table.py
@@ -0,0 +1,77 @@
+import argparse
+import json
+
+import datasets
+from surya.schema import LayoutResult, LayoutBox
+from tqdm import tqdm
+
+from marker.benchmark.table import score_table
+from marker.schema.bbox import rescale_bbox
+from marker.schema.page import Page
+from marker.tables.table import format_tables
+
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Benchmark table conversion.")
+    parser.add_argument("out_file", help="Output filename for results")
+    parser.add_argument("--dataset", type=str, help="Dataset to use", default="vikp/table_bench")
+    args = parser.parse_args()
+
+    ds = datasets.load_dataset(args.dataset, split="train")
+
+    results = []
+    for i in tqdm(range(len(ds)), desc="Evaluating tables"):
+        row = ds[i]
+        marker_page = Page(**json.loads(row["marker_page"]))
+        table_bbox = row["table_bbox"]
+        gpt4_table = json.loads(row["gpt_4_table"])["markdown_table"]
+
+        # Counterclockwise polygon from top left
+        table_poly = [
+            [table_bbox[0], table_bbox[1]],
+            [table_bbox[2], table_bbox[1]],
+            [table_bbox[2], table_bbox[3]],
+            [table_bbox[0], table_bbox[3]],
+        ]
+
+        # Remove all other tables from the layout results
+        layout_result = LayoutResult(
+            bboxes=[
+                LayoutBox(
+                    label="Table",
+                    polygon=table_poly
+                )
+            ],
+            segmentation_map="",
+            image_bbox=marker_page.text_lines.image_bbox
+        )
+
+        marker_page.layout = layout_result
+        format_tables([marker_page])
+
+        table_blocks = [block for block in marker_page.blocks if block.block_type == "Table"]
+        if len(table_blocks) != 1:
+            continue
+
+        table_block = table_blocks[0]
+        table_md = table_block.lines[0].spans[0].text
+
+        results.append({
+            "score": score_table(table_md, gpt4_table),
+            "arxiv_id": row["arxiv_id"],
+            "page_idx": row["page_idx"],
+            "marker_table": table_md,
+            "gpt4_table": gpt4_table,
+            "table_bbox": table_bbox
+        })
+
+    avg_score = sum([r["score"] for r in results]) / len(results)
+    print(f"Evaluated {len(results)} tables, average score is {avg_score}.")
+
+    with open(args.out_file, "w+") as f:
+        json.dump(results, f, indent=2)
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/convert.py b/convert.py
index 6b9ca10e..daadee9a 100755
--- a/convert.py
+++ b/convert.py
@@ -73,8 +73,8 @@ def main():
     parser.add_argument("--chunk_idx", type=int, default=0, help="Chunk index to convert")
     parser.add_argument("--num_chunks", type=int, default=1, help="Number of chunks being processed in parallel")
     parser.add_argument("--max", type=int, default=None, help="Maximum number of pdfs to convert")
-    parser.add_argument("--workers", type=int, default=5, help="Number of worker processes to use")
-    parser.add_argument("--metadata_file", type=str, default=None, help="Metadata json file to use for filtering")
+    parser.add_argument("--workers", type=int, default=5, help="Number of worker processes to use.  Peak VRAM usage per process is 5GB, but avg is closer to 3.5GB.")
+    parser.add_argument("--metadata_file", type=str, default=None, help="Metadata json file to use for languages")
     parser.add_argument("--min_length", type=int, default=None, help="Minimum length of pdf to convert")
 
     args = parser.parse_args()
@@ -104,13 +104,6 @@ def main():
 
     total_processes = min(len(files_to_convert), args.workers)
 
-    # Dynamically set GPU allocation per task based on GPU ram
-    if settings.CUDA:
-        tasks_per_gpu = settings.INFERENCE_RAM // settings.VRAM_PER_TASK if settings.CUDA else 0
-        total_processes = int(min(tasks_per_gpu, total_processes))
-    else:
-        total_processes = int(total_processes)
-
     try:
         mp.set_start_method('spawn') # Required for CUDA, forkserver doesn't work
     except RuntimeError:
diff --git a/convert_single.py b/convert_single.py
index a0af4771..eb99bf1d 100755
--- a/convert_single.py
+++ b/convert_single.py
@@ -20,9 +20,10 @@ def main():
     parser.add_argument("output", help="Output base folder path")
     parser.add_argument("--max_pages", type=int, default=None, help="Maximum number of pages to parse")
     parser.add_argument("--start_page", type=int, default=None, help="Page to start processing at")
-    parser.add_argument("--langs", type=str, help="Languages to use for OCR, comma separated", default=None)
+    parser.add_argument("--langs", type=str, help="Optional languages to use for OCR, comma separated", default=None)
     parser.add_argument("--batch_multiplier", type=int, default=2, help="How much to increase batch sizes")
     parser.add_argument("--debug", action="store_true", help="Enable debug logging", default=False)
+    parser.add_argument("--ocr_all_pages", action="store_true", help="Force OCR on all pages", default=False)
     args = parser.parse_args()
 
     langs = args.langs.split(",") if args.langs else None
@@ -30,7 +31,7 @@ def main():
     fname = args.filename
     model_lst = load_all_models()
     start = time.time()
-    full_text, images, out_meta = convert_single_pdf(fname, model_lst, max_pages=args.max_pages, langs=langs, batch_multiplier=args.batch_multiplier, start_page=args.start_page)
+    full_text, images, out_meta = convert_single_pdf(fname, model_lst, max_pages=args.max_pages, langs=langs, batch_multiplier=args.batch_multiplier, start_page=args.start_page, ocr_all_pages=args.ocr_all_pages)
 
     fname = os.path.basename(fname)
     subfolder_path = save_markdown(args.output, fname, full_text, images, out_meta)
diff --git a/marker/benchmark/scoring.py b/marker/benchmark/scoring.py
index 35f06202..4da6a19b 100644
--- a/marker/benchmark/scoring.py
+++ b/marker/benchmark/scoring.py
@@ -37,4 +37,4 @@ def score_text(hypothesis, reference):
     hypothesis_chunks = chunk_text(hypothesis)
     reference_chunks = chunk_text(reference)
     chunk_scores = overlap_score(hypothesis_chunks, reference_chunks)
-    return mean(chunk_scores)
\ No newline at end of file
+    return mean(chunk_scores)
diff --git a/marker/benchmark/table.py b/marker/benchmark/table.py
new file mode 100644
index 00000000..08233624
--- /dev/null
+++ b/marker/benchmark/table.py
@@ -0,0 +1,41 @@
+from rapidfuzz import fuzz
+import re
+
+
+def split_to_cells(table):
+    table = table.strip()
+    table = re.sub(r" {2,}", "", table)
+    table_rows = table.split("\n")
+    table_rows = [t for t in table_rows if t.strip()]
+    table_cells = [r.split("|") for r in table_rows]
+    return table_cells
+
+
+def align_rows(hypothesis, ref_row):
+    best_alignment = []
+    best_alignment_score = 0
+    for j in range(0, len(hypothesis)):
+        alignments = []
+        for i in range(len(ref_row)):
+            if i >= len(hypothesis[j]):
+                alignments.append(0)
+                continue
+            alignment = fuzz.ratio(hypothesis[j][i], ref_row[i], score_cutoff=30) / 100
+            alignments.append(alignment)
+        if len(alignments) == 0:
+            continue
+        alignment_score = sum(alignments) / len(alignments)
+        if alignment_score >= best_alignment_score:
+            best_alignment = alignments
+            best_alignment_score = alignment_score
+    return best_alignment
+
+
+def score_table(hypothesis, reference):
+    hypothesis = split_to_cells(hypothesis)
+    reference = split_to_cells(reference)
+
+    alignments = []
+    for i in range(0, len(reference)):
+        alignments.extend(align_rows(hypothesis, reference[i]))
+    return sum(alignments) / len(alignments)
\ No newline at end of file
diff --git a/marker/convert.py b/marker/convert.py
index 0074b90d..3c56fc9c 100644
--- a/marker/convert.py
+++ b/marker/convert.py
@@ -41,11 +41,10 @@ def convert_single_pdf(
         start_page: int = None,
         metadata: Optional[Dict] = None,
         langs: Optional[List[str]] = None,
-        batch_multiplier: int = 1
+        batch_multiplier: int = 1,
+        ocr_all_pages: bool = False
 ) -> Tuple[str, Dict[str, Image.Image], Dict]:
-    # Set language needed for OCR
-    if langs is None:
-        langs = [settings.DEFAULT_LANG]
+    ocr_all_pages = ocr_all_pages or settings.OCR_ALL_PAGES
 
     if metadata:
         langs = metadata.get("languages", langs)
@@ -91,7 +90,7 @@ def convert_single_pdf(
     flush_cuda_memory()
 
     # OCR pages as needed
-    pages, ocr_stats = run_ocr(doc, pages, langs, ocr_model, batch_multiplier=batch_multiplier)
+    pages, ocr_stats = run_ocr(doc, pages, langs, ocr_model, batch_multiplier=batch_multiplier, ocr_all_pages=ocr_all_pages)
     flush_cuda_memory()
 
     out_meta["ocr_stats"] = ocr_stats
diff --git a/marker/equations/inference.py b/marker/equations/inference.py
index b37920fe..eb904ad6 100644
--- a/marker/equations/inference.py
+++ b/marker/equations/inference.py
@@ -1,4 +1,5 @@
 from texify.inference import batch_inference
+from tqdm import tqdm
 
 from marker.settings import settings
 import os
@@ -22,7 +23,7 @@ def get_latex_batched(images, token_counts, texify_model, batch_multiplier=1):
     predictions = [""] * len(images)
     batch_size = get_batch_size() * batch_multiplier
 
-    for i in range(0, len(images), batch_size):
+    for i in tqdm(range(0, len(images), batch_size), desc="Recognizing equations"):
         # Dynamically set max length to save inference time
         min_idx = i
         max_idx = min(min_idx + batch_size, len(images))
diff --git a/marker/models.py b/marker/models.py
index ba7b2866..4764d84e 100644
--- a/marker/models.py
+++ b/marker/models.py
@@ -13,11 +13,11 @@
 from surya.model.ordering.processor import load_processor as load_order_processor
 
 
-def setup_recognition_model(langs, device=None, dtype=None):
+def setup_recognition_model(device=None, dtype=None):
     if device:
-        rec_model = load_recognition_model(langs=langs, device=device, dtype=dtype)
+        rec_model = load_recognition_model(device=device, dtype=dtype)
     else:
-        rec_model = load_recognition_model(langs=langs)
+        rec_model = load_recognition_model()
     rec_processor = load_recognition_processor()
     rec_model.processor = rec_processor
     return rec_model
@@ -64,7 +64,7 @@ def setup_order_model(device=None, dtype=None):
     return model
 
 
-def load_all_models(langs=None, device=None, dtype=None, force_load_ocr=False):
+def load_all_models(device=None, dtype=None, force_load_ocr=False):
     if device is not None:
         assert dtype is not None, "Must provide dtype if device is provided"
 
@@ -75,7 +75,7 @@ def load_all_models(langs=None, device=None, dtype=None, force_load_ocr=False):
     edit = load_editing_model(device, dtype)
 
     # Only load recognition model if we'll need it for all pdfs
-    ocr = setup_recognition_model(langs, device, dtype)
+    ocr = setup_recognition_model(device, dtype)
     texify = setup_texify_model(device, dtype)
     model_lst = [texify, layout, order, edit, detection, ocr]
     return model_lst
\ No newline at end of file
diff --git a/marker/ocr/heuristics.py b/marker/ocr/heuristics.py
index b3cf6b2b..0fa063cf 100644
--- a/marker/ocr/heuristics.py
+++ b/marker/ocr/heuristics.py
@@ -7,7 +7,7 @@
 from marker.settings import settings
 
 
-def should_ocr_page(page: Page, no_text: bool):
+def should_ocr_page(page: Page, no_text: bool, ocr_all_pages=False):
     detected_lines_found, total_lines = detected_line_coverage(page)
 
     # No reason to OCR page if it has no text lines
@@ -21,7 +21,7 @@ def should_ocr_page(page: Page, no_text: bool):
         detected_lines_found is False, # didn't extract text for all detected lines
     ]
 
-    return any(conditions) or settings.OCR_ALL_PAGES
+    return any(conditions) or ocr_all_pages
 
 
 def detect_bad_ocr(text, space_threshold=.7, newline_threshold=.6, alphanum_threshold=.3):
diff --git a/marker/ocr/lang.py b/marker/ocr/lang.py
index 3e49004c..ef939335 100644
--- a/marker/ocr/lang.py
+++ b/marker/ocr/lang.py
@@ -15,10 +15,16 @@ def langs_to_ids(langs: List[str]):
 
 def replace_langs_with_codes(langs):
     if settings.OCR_ENGINE == "surya":
+        if langs is None:
+            return
         for i, lang in enumerate(langs):
             if lang.title() in LANGUAGE_TO_CODE:
                 langs[i] = LANGUAGE_TO_CODE[lang.title()]
     else:
+        if langs is None:
+            langs = [settings.DEFAULT_LANG]
+            print(f"No languages specified for tesseract, defaulting to {settings.DEFAULT_LANG}.")
+
         for i, lang in enumerate(langs):
             if lang in LANGUAGE_TO_CODE:
                 langs[i] = LANGUAGE_TO_TESSERACT_CODE[lang]
@@ -27,6 +33,8 @@ def replace_langs_with_codes(langs):
 
 def validate_langs(langs):
     if settings.OCR_ENGINE == "surya":
+        if langs is None:
+            return
         for lang in langs:
             if lang not in CODE_TO_LANGUAGE:
                 raise ValueError(f"Invalid language code {lang} for Surya OCR")
diff --git a/marker/ocr/recognition.py b/marker/ocr/recognition.py
index 20ff0fd8..44c22b12 100644
--- a/marker/ocr/recognition.py
+++ b/marker/ocr/recognition.py
@@ -28,14 +28,14 @@ def get_batch_size():
     return 32
 
 
-def run_ocr(doc, pages: List[Page], langs: List[str], rec_model, batch_multiplier=1) -> (List[Page], Dict):
+def run_ocr(doc, pages: List[Page], langs: List[str], rec_model, batch_multiplier=1, ocr_all_pages=False) -> (List[Page], Dict):
     ocr_pages = 0
     ocr_success = 0
     ocr_failed = 0
     no_text = no_text_found(pages)
     ocr_idxs = []
     for pnum, page in enumerate(pages):
-        ocr_needed = should_ocr_page(page, no_text)
+        ocr_needed = should_ocr_page(page, no_text, ocr_all_pages=ocr_all_pages)
         if ocr_needed:
             ocr_idxs.append(pnum)
             ocr_pages += 1
@@ -45,7 +45,7 @@ def run_ocr(doc, pages: List[Page], langs: List[str], rec_model, batch_multiplie
         return pages, {"ocr_pages": 0, "ocr_failed": 0, "ocr_success": 0, "ocr_engine": "none"}
 
     ocr_method = settings.OCR_ENGINE
-    if ocr_method is None:
+    if ocr_method is None or ocr_method == "None":
         return pages, {"ocr_pages": 0, "ocr_failed": 0, "ocr_success": 0, "ocr_engine": "none"}
     elif ocr_method == "surya":
         new_pages = surya_recognition(doc, ocr_idxs, langs, rec_model, pages, batch_multiplier=batch_multiplier)
diff --git a/marker/settings.py b/marker/settings.py
index cd1d1913..6d6bf7e7 100644
--- a/marker/settings.py
+++ b/marker/settings.py
@@ -27,8 +27,6 @@ def TORCH_DEVICE_MODEL(self) -> str:
 
         return "cpu"
 
-    INFERENCE_RAM: int = 40 # How much VRAM each GPU has (in GB).
-    VRAM_PER_TASK: float = 4.5 # How much VRAM to allocate per task (in GB).  Peak marker VRAM usage is around 5GB, but avg across workers is lower.
     DEFAULT_LANG: str = "English" # Default language we assume files to be in, should be one of the keys in TESSERACT_LANGUAGES
 
     SUPPORTED_FILETYPES: Dict = {
@@ -66,9 +64,10 @@ def TORCH_DEVICE_MODEL(self) -> str:
 
     # Layout model
     SURYA_LAYOUT_DPI: int = 96
-    BAD_SPAN_TYPES: List[str] = ["Caption", "Footnote", "Page-footer", "Page-header", "Picture"]
+    BAD_SPAN_TYPES: List[str] = ["Page-footer", "Page-header", "Picture"] # You can add "Caption" and "Footnote" here to get rid of those elements
     LAYOUT_MODEL_CHECKPOINT: str = "vikp/surya_layout3"
     BBOX_INTERSECTION_THRESH: float = 0.7 # How much the layout and pdf bboxes need to overlap to be the same
+    TABLE_INTERSECTION_THRESH: float = 0.7
     LAYOUT_BATCH_SIZE: Optional[int] = None # Defaults to 12 for cuda, 6 otherwise
 
     # Ordering model
diff --git a/marker/tables/cells.py b/marker/tables/cells.py
index 2b119493..46d83484 100644
--- a/marker/tables/cells.py
+++ b/marker/tables/cells.py
@@ -1,17 +1,16 @@
 from marker.schema.bbox import rescale_bbox, box_intersection_pct
 from marker.schema.page import Page
-from sklearn.cluster import DBSCAN
 import numpy as np
-
+from sklearn.cluster import DBSCAN
 from marker.settings import settings
 
 
-def cluster_coords(coords):
+def cluster_coords(coords, row_count):
     if len(coords) == 0:
         return []
     coords = np.array(sorted(set(coords))).reshape(-1, 1)
 
-    clustering = DBSCAN(eps=5, min_samples=1).fit(coords)
+    clustering = DBSCAN(eps=.01, min_samples=max(2, row_count // 4)).fit(coords)
     clusters = clustering.labels_
 
     separators = []
@@ -23,7 +22,7 @@ def cluster_coords(coords):
     return separators
 
 
-def find_column_separators(page: Page, table_box, round_factor=4, min_count=1):
+def find_column_separators(page: Page, table_box, rows, round_factor=.002, min_count=1):
     left_edges = []
     right_edges = []
     centers = []
@@ -32,35 +31,40 @@ def find_column_separators(page: Page, table_box, round_factor=4, min_count=1):
     line_boxes = [rescale_bbox(page.text_lines.image_bbox, page.bbox, l) for l in line_boxes]
     line_boxes = [l for l in line_boxes if box_intersection_pct(l, table_box) > settings.BBOX_INTERSECTION_THRESH]
 
+    pwidth = page.bbox[2] - page.bbox[0]
+    pheight = page.bbox[3] - page.bbox[1]
     for cell in line_boxes:
-        left_edges.append(cell[0] / round_factor * round_factor)
-        right_edges.append(cell[2] / round_factor * round_factor)
-        centers.append((cell[0] + cell[2]) / 2 * round_factor / round_factor)
+        ncell = [cell[0] / pwidth, cell[1] / pheight, cell[2] / pwidth, cell[3] / pheight]
+        left_edges.append(ncell[0] / round_factor * round_factor)
+        right_edges.append(ncell[2] / round_factor * round_factor)
+        centers.append((ncell[0] + ncell[2]) / 2 * round_factor / round_factor)
 
     left_edges = [l for l in left_edges if left_edges.count(l) > min_count]
     right_edges = [r for r in right_edges if right_edges.count(r) > min_count]
     centers = [c for c in centers if centers.count(c) > min_count]
 
-    sorted_left = cluster_coords(left_edges)
-    sorted_right = cluster_coords(right_edges)
-    sorted_center = cluster_coords(centers)
+    sorted_left = cluster_coords(left_edges, len(rows))
+    sorted_right = cluster_coords(right_edges, len(rows))
+    sorted_center = cluster_coords(centers, len(rows))
 
     # Find list with minimum length
     separators = max([sorted_left, sorted_right, sorted_center], key=len)
-    separators.append(page.bbox[2])
-    separators.insert(0, page.bbox[0])
+    separators.append(1)
+    separators.insert(0, 0)
     return separators
 
 
-def assign_cells_to_columns(page, table_box, rows, round_factor=4, tolerance=4):
-    separators = find_column_separators(page, table_box, round_factor=round_factor)
-    new_rows = []
+def assign_cells_to_columns(page, table_box, rows, round_factor=.002, tolerance=.01):
+    separators = find_column_separators(page, table_box, rows, round_factor=round_factor)
     additional_column_index = 0
+    pwidth = page.bbox[2] - page.bbox[0]
+    row_dicts = []
+
     for row in rows:
         new_row = {}
         last_col_index = -1
         for cell in row:
-            left_edge = cell[0][0]
+            left_edge = cell[0][0] / pwidth
             column_index = -1
             for i, separator in enumerate(separators):
                 if left_edge - tolerance < separator and last_col_index < i:
@@ -72,10 +76,21 @@ def assign_cells_to_columns(page, table_box, rows, round_factor=4, tolerance=4):
             new_row[column_index] = cell[1]
             last_col_index = column_index
         additional_column_index = 0
+        row_dicts.append(new_row)
+
+    max_row_idx = 0
+    for row in row_dicts:
+        max_row_idx = max(max_row_idx, max(row.keys()))
 
+    # Assign sorted cells to columns, account for blanks
+    new_rows = []
+    for row in row_dicts:
         flat_row = []
-        for cell_idx, cell in enumerate(sorted(new_row.items())):
-            flat_row.append(cell[1])
+        for row_idx in range(1, max_row_idx + 1):
+            if row_idx in row:
+                flat_row.append(row[row_idx])
+            else:
+                flat_row.append("")
         new_rows.append(flat_row)
 
     # Pad rows to have the same length
@@ -94,4 +109,4 @@ def assign_cells_to_columns(page, table_box, rows, round_factor=4, tolerance=4):
     for row in new_rows:
         rows.append([col for idx, col in enumerate(row) if idx not in cols_to_remove])
 
-    return rows
+    return rows
\ No newline at end of file
diff --git a/marker/tables/edges.py b/marker/tables/edges.py
new file mode 100644
index 00000000..9bf30ba1
--- /dev/null
+++ b/marker/tables/edges.py
@@ -0,0 +1,122 @@
+import math
+
+import cv2
+import numpy as np
+
+
+def get_detected_lines_sobel(image):
+    sobelx = cv2.Sobel(image, cv2.CV_32F, 1, 0, ksize=3)
+
+    scaled_sobel = np.uint8(255 * sobelx / np.max(sobelx))
+
+    kernel = np.ones((4, 1), np.uint8)
+    eroded = cv2.erode(scaled_sobel, kernel, iterations=1)
+    scaled_sobel = cv2.dilate(eroded, kernel, iterations=3)
+
+    return scaled_sobel
+
+
+def get_line_angle(x1, y1, x2, y2):
+    slope = (y2 - y1) / (x2 - x1)
+
+    angle_radians = math.atan(slope)
+    angle_degrees = math.degrees(angle_radians)
+
+    return angle_degrees
+
+
+def get_detected_lines(image, slope_tol_deg=10):
+    new_image = image.astype(np.float32) * 255  # Convert to 0-255 range
+    new_image = get_detected_lines_sobel(new_image)
+    new_image = new_image.astype(np.uint8)
+
+    edges = cv2.Canny(new_image, 50, 200, apertureSize=3)
+
+    lines = cv2.HoughLinesP(edges, 1, np.pi / 180, threshold=50, minLineLength=2, maxLineGap=100)
+
+    line_info = []
+    if lines is not None:
+        for line in lines:
+            x1, y1, x2, y2 = line[0]
+            bbox = [x1, y1, x2, y2]
+
+            vertical = False
+            if x2 == x1:
+                vertical = True
+            else:
+                line_angle = get_line_angle(x1, y1, x2, y2)
+                if 90 - slope_tol_deg < line_angle < 90 + slope_tol_deg:
+                    vertical = True
+                elif -90 - slope_tol_deg < line_angle < -90 + slope_tol_deg:
+                    vertical = True
+            if not vertical:
+                continue
+
+            if bbox[3] < bbox[1]:
+                bbox[1], bbox[3] = bbox[3], bbox[1]
+            if bbox[2] < bbox[0]:
+                bbox[0], bbox[2] = bbox[2], bbox[0]
+            if vertical:
+                line_info.append(bbox)
+    return line_info
+
+
+def get_vertical_lines(image, divisor=2, x_tolerance=10, y_tolerance=1):
+    vertical_lines = get_detected_lines(image)
+
+    vertical_lines = sorted(vertical_lines, key=lambda x: x[0])
+    for line in vertical_lines:
+        for i in range(0, len(line)):
+            line[i] = (line[i] // divisor) * divisor
+
+    # Merge adjacent line segments together
+    to_remove = []
+    for i, line in enumerate(vertical_lines):
+        for j, line2 in enumerate(vertical_lines):
+            if j <= i:
+                continue
+            if line[0] != line2[0]:
+                continue
+
+            expanded_line1 = [line[0], line[1] - y_tolerance, line[2],
+                              line[3] + y_tolerance]
+
+            line1_points = set(range(int(expanded_line1[1]), int(expanded_line1[3])))
+            line2_points = set(range(int(line2[1]), int(line2[3])))
+            intersect_y = len(line1_points.intersection(line2_points)) > 0
+
+            if intersect_y:
+                vertical_lines[j][1] = min(line[1], line2[1])
+                vertical_lines[j][3] = max(line[3], line2[3])
+                to_remove.append(i)
+
+    vertical_lines = [line for i, line in enumerate(vertical_lines) if i not in to_remove]
+
+    # Remove redundant segments
+    to_remove = []
+    for i, line in enumerate(vertical_lines):
+        if i in to_remove:
+            continue
+        for j, line2 in enumerate(vertical_lines):
+            if j <= i or j in to_remove:
+                continue
+            close_in_x = abs(line[0] - line2[0]) < x_tolerance
+            line1_points = set(range(int(line[1]), int(line[3])))
+            line2_points = set(range(int(line2[1]), int(line2[3])))
+
+            intersect_y = len(line1_points.intersection(line2_points)) > 0
+
+            if close_in_x and intersect_y:
+                # Keep the longer line and extend it
+                if len(line2_points) > len(line1_points):
+                    vertical_lines[j][1] = min(line[1], line2[1])
+                    vertical_lines[j][3] = max(line[3], line2[3])
+                    to_remove.append(i)
+                else:
+                    vertical_lines[i][1] = min(line[1], line2[1])
+                    vertical_lines[i][3] = max(line[3], line2[3])
+                    to_remove.append(j)
+
+    vertical_lines = [line for i, line in enumerate(vertical_lines) if i not in to_remove]
+
+    return vertical_lines
\ No newline at end of file
diff --git a/marker/tables/table.py b/marker/tables/table.py
index 736089a6..280ae2a5 100644
--- a/marker/tables/table.py
+++ b/marker/tables/table.py
@@ -19,7 +19,7 @@ def get_table_surya(page, table_box, space_tol=.01) -> List[List[str]]:
         for line_idx, line in enumerate(sorted_lines):
             line_bbox = line.bbox
             intersect_pct = box_intersection_pct(line_bbox, table_box)
-            if intersect_pct < .5 or len(line.spans) == 0:
+            if intersect_pct < settings.TABLE_INTERSECTION_THRESH or len(line.spans) == 0:
                 continue
             normed_x_start = line_bbox[0] / page.width
             normed_x_end = line_bbox[2] / page.width
@@ -49,20 +49,22 @@ def get_table_pdftext(page: Page, table_box, space_tol=.01, round_factor=4) -> L
     sorted_char_blocks = sort_table_blocks(page.char_blocks)
 
     table_width = table_box[2] - table_box[0]
-    new_line_start_x = table_box[0] + table_width * .2
+    new_line_start_x = table_box[0] + table_width * .3
+    table_width_pct = (table_width / page_width) * .95
 
     for block_idx, block in enumerate(sorted_char_blocks):
         sorted_lines = sort_table_blocks(block["lines"])
         for line_idx, line in enumerate(sorted_lines):
             line_bbox = line["bbox"]
             intersect_pct = box_intersection_pct(line_bbox, table_box)
-            if intersect_pct < settings.BBOX_INTERSECTION_THRESH:
+            if intersect_pct < settings.TABLE_INTERSECTION_THRESH:
                 continue
             for span in line["spans"]:
                 for char in span["chars"]:
                     x_start, y_start, x_end, y_end = char["bbox"]
                     x_start /= page_width
                     x_end /= page_width
+                    fullwidth_cell = False
 
                     if cell_bbox is not None:
                         # Find boundaries of cell bbox before merging
@@ -70,11 +72,14 @@ def get_table_pdftext(page: Page, table_box, space_tol=.01, round_factor=4) -> L
                         cell_x_start /= page_width
                         cell_x_end /= page_width
 
+                        fullwidth_cell = cell_x_end - cell_x_start >= table_width_pct
+
                     cell_content = replace_dots(replace_newlines(table_cell))
                     if cell_bbox is None: # First char
                         table_cell += char["char"]
                         cell_bbox = char["bbox"]
-                    elif cell_x_start - space_tol < x_start < cell_x_end + space_tol: # Check if we are in the same cell
+                    # Check if we are in the same cell, ensure cell is not full table width (like if stray text gets included in the table)
+                    elif (cell_x_start - space_tol < x_start < cell_x_end + space_tol) and not fullwidth_cell:
                         table_cell += char["char"]
                         cell_bbox = merge_boxes(cell_bbox, char["bbox"])
                     # New line and cell
@@ -100,8 +105,34 @@ def get_table_pdftext(page: Page, table_box, space_tol=.01, round_factor=4) -> L
         table_row = sorted(table_row, key=lambda x: round(x[0][0] / round_factor))
         table_rows.append(table_row)
 
-    table_rows = assign_cells_to_columns(page, table_box, table_rows)
-    return table_rows
+    total_cells = sum([len(row) for row in table_rows])
+    if total_cells > 0:
+        table_rows = assign_cells_to_columns(page, table_box, table_rows)
+        return table_rows
+    else:
+        return []
+
+
+def merge_tables(page_table_boxes):
+    # Merge tables that are next to each other
+    expansion_factor = 1.02
+    shrink_factor = .98
+    ignore_boxes = set()
+    for i in range(len(page_table_boxes)):
+        if i in ignore_boxes:
+            continue
+        for j in range(i + 1, len(page_table_boxes)):
+            if j in ignore_boxes:
+                continue
+            expanded_box1 = [page_table_boxes[i][0] * shrink_factor, page_table_boxes[i][1],
+                             page_table_boxes[i][2] * expansion_factor, page_table_boxes[i][3]]
+            expanded_box2 = [page_table_boxes[j][0] * shrink_factor, page_table_boxes[j][1],
+                             page_table_boxes[j][2] * expansion_factor, page_table_boxes[j][3]]
+            if box_intersection_pct(expanded_box1, expanded_box2) > 0:
+                page_table_boxes[i] = merge_boxes(page_table_boxes[i], page_table_boxes[j])
+                ignore_boxes.add(j)
+
+    return [b for i, b in enumerate(page_table_boxes) if i not in ignore_boxes]
 
 
 def format_tables(pages: List[Page]):
@@ -114,12 +145,14 @@ def format_tables(pages: List[Page]):
 
         page_table_boxes = [b for b in page.layout.bboxes if b.label == "Table"]
         page_table_boxes = [rescale_bbox(page.layout.image_bbox, page.bbox, b.bbox) for b in page_table_boxes]
+        page_table_boxes = merge_tables(page_table_boxes)
+
         for table_idx, table_box in enumerate(page_table_boxes):
             for block_idx, block in enumerate(page.blocks):
                 intersect_pct = block.intersection_pct(table_box)
-                if intersect_pct > settings.BBOX_INTERSECTION_THRESH and block.block_type == "Table":
+                if intersect_pct > settings.TABLE_INTERSECTION_THRESH and block.block_type == "Table":
                     if table_idx not in table_insert_points:
-                        table_insert_points[table_idx] = block_idx - len(blocks_to_remove) + table_idx # Where to insert the new table
+                        table_insert_points[table_idx] = max(0, block_idx - len(blocks_to_remove)) # Where to insert the new table
                     blocks_to_remove.add(block_idx)
 
         new_page_blocks = []
@@ -159,6 +192,7 @@ def format_tables(pages: List[Page]):
                 )]
             )
             insert_point = table_insert_points[table_idx]
+            insert_point = min(insert_point, len(new_page_blocks))
             new_page_blocks.insert(insert_point, table_block)
             table_count += 1
         page.blocks = new_page_blocks
diff --git a/marker/tables/utils.py b/marker/tables/utils.py
index 61b03403..2148ca28 100644
--- a/marker/tables/utils.py
+++ b/marker/tables/utils.py
@@ -8,7 +8,7 @@ def sort_table_blocks(blocks, tolerance=5):
             bbox = block.bbox
         else:
             bbox = block["bbox"]
-        group_key = round(bbox[1] / tolerance)
+        group_key = round((bbox[1] + bbox[3]) / 2 / tolerance)
         if group_key not in vertical_groups:
             vertical_groups[group_key] = []
         vertical_groups[group_key].append(block)
@@ -34,4 +34,4 @@ def replace_dots(text):
 def replace_newlines(text):
     # Replace all newlines
     newline_pattern = re.compile(r'[\r\n]+')
-    return newline_pattern.sub(' ', text.strip())
+    return newline_pattern.sub(' ', text).strip()
\ No newline at end of file
diff --git a/marker_app.py b/marker_app.py
new file mode 100644
index 00000000..6b8e59b0
--- /dev/null
+++ b/marker_app.py
@@ -0,0 +1,118 @@
+import os
+os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
+os.environ["IN_STREAMLIT"] = "true"
+
+import base64
+import io
+import re
+import tempfile
+from typing import List, Any, Dict
+
+import pypdfium2
+import streamlit as st
+
+from marker.convert import convert_single_pdf
+from marker.models import load_all_models
+from surya.languages import CODE_TO_LANGUAGE
+
+@st.cache_resource()
+def load_models():
+    return load_all_models()
+
+
+def convert_pdf(fname: str, langs: List[str] | None, max_pages: int | None, ocr_all_pages: bool) -> (str, Dict[str, Any], dict):
+    full_text, images, out_meta = convert_single_pdf(fname, model_lst, max_pages=max_pages, langs=langs, ocr_all_pages=ocr_all_pages)
+    return full_text, images, out_meta
+
+
+def open_pdf(pdf_file):
+    stream = io.BytesIO(pdf_file.getvalue())
+    return pypdfium2.PdfDocument(stream)
+
+
+def img_to_html(img, img_alt):
+    img_bytes = io.BytesIO()
+    img.save(img_bytes, format="PNG")
+    img_bytes = img_bytes.getvalue()
+    encoded = base64.b64encode(img_bytes).decode()
+    img_html = f'<img src="data:image/png;base64,{encoded}" alt="{img_alt}" style="max-width: 100%;">'
+    return img_html
+
+
+def markdown_insert_images(markdown, images):
+    image_tags = re.findall(r'(!\[(?P<image_title>[^\]]+)\]\((?P<image_path>[^\)"\s]+)\s*([^\)]*)\))', markdown)
+
+    for image in image_tags:
+        image_markdown = image[0]
+        image_alt = image[1]
+        image_path = image[2]
+        if image_path in images:
+            markdown = markdown.replace(image_markdown, img_to_html(images[image_path], image_alt))
+    return markdown
+
+
+@st.cache_data()
+def get_page_image(pdf_file, page_num, dpi=96):
+    doc = open_pdf(pdf_file)
+    renderer = doc.render(
+        pypdfium2.PdfBitmap.to_pil,
+        page_indices=[page_num - 1],
+        scale=dpi / 72,
+    )
+    png = list(renderer)[0]
+    png_image = png.convert("RGB")
+    return png_image
+
+
+@st.cache_data()
+def page_count(pdf_file):
+    doc = open_pdf(pdf_file)
+    return len(doc)
+
+
+st.set_page_config(layout="wide")
+col1, col2 = st.columns([.5, .5])
+
+model_lst = load_models()
+
+
+st.markdown("""
+# Marker Demo
+
+This app will let you try marker, a PDF -> Markdown converter. It works with any languages, and extracts images, tables, equations, etc.
+
+Find the project [here](https://github.com/VikParuchuri/marker).
+""")
+
+in_file = st.sidebar.file_uploader("PDF file:", type=["pdf"])
+languages = st.sidebar.multiselect("Languages", sorted(list(CODE_TO_LANGUAGE.values())), default=[], max_selections=4, help="Select the languages in the pdf (if known) to improve OCR accuracy.  Optional.")
+max_pages = st.sidebar.number_input("Max pages to parse", min_value=1, value=10, help="Optional maximum number of pages to convert")
+ocr_all_pages = st.sidebar.checkbox("Force OCR on all pages", help="Force OCR on all pages, even if they are images", value=False)
+
+if in_file is None:
+    st.stop()
+
+filetype = in_file.type
+
+with col1:
+    page_count = page_count(in_file)
+    page_number = st.number_input(f"Page number out of {page_count}:", min_value=1, value=1, max_value=page_count)
+    pil_image = get_page_image(in_file, page_number)
+
+    st.image(pil_image, caption="PDF file (preview)", use_column_width=True)
+
+run_marker = st.sidebar.button("Run Marker")
+
+if not run_marker:
+    st.stop()
+
+# Run Marker
+with tempfile.NamedTemporaryFile(suffix=".pdf") as temp_pdf:
+    temp_pdf.write(in_file.getvalue())
+    temp_pdf.seek(0)
+    filename = temp_pdf.name
+    md_text, images, out_meta = convert_pdf(filename, languages, max_pages, ocr_all_pages)
+md_text = markdown_insert_images(md_text, images)
+with col2:
+    st.markdown(md_text, unsafe_allow_html=True)
+
diff --git a/poetry.lock b/poetry.lock
index abbc24ca..c4be59e9 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,5 +1,150 @@
 # This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
 
+[[package]]
+name = "aiohappyeyeballs"
+version = "2.4.0"
+description = "Happy Eyeballs for asyncio"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "aiohappyeyeballs-2.4.0-py3-none-any.whl", hash = "sha256:7ce92076e249169a13c2f49320d1967425eaf1f407522d707d59cac7628d62bd"},
+    {file = "aiohappyeyeballs-2.4.0.tar.gz", hash = "sha256:55a1714f084e63d49639800f95716da97a1f173d46a16dfcfda0016abb93b6b2"},
+]
+
+[[package]]
+name = "aiohttp"
+version = "3.10.4"
+description = "Async http client/server framework (asyncio)"
+optional = false
+python-versions = ">=3.8"
+files = [
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 version = "0.7.0"
@@ -152,6 +297,17 @@ files = [
 [package.dependencies]
 typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.11\""}
 
+[[package]]
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 [package.extras]
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+[package.dependencies]
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+
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@@ -426,6 +618,50 @@ traitlets = ">=4"
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 test = ["pytest"]
 
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+
 [[package]]
 name = "fsspec"
 version = "2024.6.1"
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+aiohttp = {version = "<4.0.0a0 || >4.0.0a0,<4.0.0a1 || >4.0.0a1", optional = true, markers = "extra == \"http\""}
+
 [package.extras]
 abfs = ["adlfs"]
 adl = ["adlfs"]
@@ -612,6 +952,38 @@ files = [
 [package.dependencies]
 wcwidth = ">=0.2.12,<0.3.0"
 
+[[package]]
+name = "gitdb"
+version = "4.0.11"
+description = "Git Object Database"
+optional = false
+python-versions = ">=3.7"
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+[package.dependencies]
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+
+[[package]]
+name = "gitpython"
+version = "3.1.43"
+description = "GitPython is a Python library used to interact with Git repositories"
+optional = false
+python-versions = ">=3.7"
+files = [
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+
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+
 [[package]]
 name = "grpcio"
 version = "1.64.1"
@@ -1286,6 +1658,30 @@ files = [
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+description = "Python port of markdown-it. Markdown parsing, done right!"
+optional = false
+python-versions = ">=3.8"
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+
+[package.dependencies]
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+
+[package.extras]
+benchmarking = ["psutil", "pytest", "pytest-benchmark"]
+code-style = ["pre-commit (>=3.0,<4.0)"]
+compare = ["commonmark (>=0.9,<1.0)", "markdown (>=3.4,<4.0)", "mistletoe (>=1.0,<2.0)", "mistune (>=2.0,<3.0)", "panflute (>=2.3,<3.0)"]
+linkify = ["linkify-it-py (>=1,<3)"]
+plugins = ["mdit-py-plugins"]
+profiling = ["gprof2dot"]
+rtd = ["jupyter_sphinx", "mdit-py-plugins", "myst-parser", "pyyaml", "sphinx", "sphinx-copybutton", "sphinx-design", "sphinx_book_theme"]
+testing = ["coverage", "pytest", "pytest-cov", "pytest-regressions"]
+
 [[package]]
 name = "markupsafe"
 version = "2.1.5"
@@ -1369,6 +1765,17 @@ files = [
 [package.dependencies]
 traitlets = "*"
 
+[[package]]
+name = "mdurl"
+version = "0.1.2"
+description = "Markdown URL utilities"
+optional = false
+python-versions = ">=3.7"
+files = [
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+]
+
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 name = "mistune"
 version = "3.0.2"
@@ -1415,6 +1822,146 @@ docs = ["sphinx"]
 gmpy = ["gmpy2 (>=2.1.0a4)"]
 tests = ["pytest (>=4.6)"]
 
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+description = "multidict implementation"
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+python-versions = ">=3.7"
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@@ -1998,6 +2618,26 @@ files = [
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 wcwidth = "*"
 
+[[package]]
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 tests = ["pytest"]
 
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+
+[package.dependencies]
+numpy = ">=1.16.6"
+
+[package.extras]
+test = ["cffi", "hypothesis", "pandas", "pytest", "pytz"]
+
 [[package]]
 name = "pycparser"
 version = "2.22"
@@ -2185,13 +2876,13 @@ typing-extensions = ">=4.6.0,<4.7.0 || >4.7.0"
 
 [[package]]
 name = "pydantic-settings"
-version = "2.3.4"
+version = "2.4.0"
 description = "Settings management using Pydantic"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -2199,9 +2890,29 @@ pydantic = ">=2.7.0"
 python-dotenv = ">=0.21.0"
 
 [package.extras]
+azure-key-vault = ["azure-identity (>=1.16.0)", "azure-keyvault-secrets (>=4.8.0)"]
 toml = ["tomli (>=2.0.1)"]
 yaml = ["pyyaml (>=6.0.1)"]
 
+[[package]]
+name = "pydeck"
+version = "0.9.1"
+description = "Widget for deck.gl maps"
+optional = false
+python-versions = ">=3.8"
+files = [
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+]
+
+[package.dependencies]
+jinja2 = ">=2.10.1"
+numpy = ">=1.16.4"
+
+[package.extras]
+carto = ["pydeck-carto"]
+jupyter = ["ipykernel (>=5.1.2)", "ipython (>=5.8.0)", "ipywidgets (>=7,<8)", "traitlets (>=4.3.2)"]
+
 [[package]]
 name = "pygments"
 version = "2.18.0"
@@ -2277,6 +2988,17 @@ files = [
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 ]
 
+[[package]]
+name = "pytz"
+version = "2024.1"
+description = "World timezone definitions, modern and historical"
+optional = false
+python-versions = "*"
+files = [
+    {file = "pytz-2024.1-py2.py3-none-any.whl", hash = "sha256:328171f4e3623139da4983451950b28e95ac706e13f3f2630a879749e7a8b319"},
+    {file = "pytz-2024.1.tar.gz", hash = "sha256:2a29735ea9c18baf14b448846bde5a48030ed267578472d8955cd0e7443a9812"},
+]
+
 [[package]]
 name = "pywin32"
 version = "306"
@@ -2771,6 +3493,24 @@ files = [
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 ]
 
+[[package]]
+name = "rich"
+version = "13.7.1"
+description = "Render rich text, tables, progress bars, syntax highlighting, markdown and more to the terminal"
+optional = false
+python-versions = ">=3.7.0"
+files = [
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+    {file = "rich-13.7.1.tar.gz", hash = "sha256:9be308cb1fe2f1f57d67ce99e95af38a1e2bc71ad9813b0e247cf7ffbcc3a432"},
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+
+[package.dependencies]
+markdown-it-py = ">=2.2.0"
+pygments = ">=2.13.0,<3.0.0"
+
+[package.extras]
+jupyter = ["ipywidgets (>=7.5.1,<9)"]
+
 [[package]]
 name = "rpds-py"
 version = "0.19.0"
@@ -3127,6 +3867,17 @@ files = [
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 ]
 
+[[package]]
+name = "smmap"
+version = "5.0.1"
+description = "A pure Python implementation of a sliding window memory map manager"
+optional = false
+python-versions = ">=3.7"
+files = [
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+    {file = "smmap-5.0.1.tar.gz", hash = "sha256:dceeb6c0028fdb6734471eb07c0cd2aae706ccaecab45965ee83f11c8d3b1f62"},
+]
+
 [[package]]
 name = "sniffio"
 version = "1.3.1"
@@ -3168,15 +3919,50 @@ pure-eval = "*"
 [package.extras]
 tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"]
 
+[[package]]
+name = "streamlit"
+version = "1.37.1"
+description = "A faster way to build and share data apps"
+optional = false
+python-versions = "!=3.9.7,>=3.8"
+files = [
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+    {file = "streamlit-1.37.1.tar.gz", hash = "sha256:bc7e3813d94a39dda56f15678437eb37830973c601e8e574f2225a7bf188ea5a"},
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+
+[package.dependencies]
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+blinker = ">=1.0.0,<2"
+cachetools = ">=4.0,<6"
+click = ">=7.0,<9"
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+typing-extensions = ">=4.3.0,<5"
+watchdog = {version = ">=2.1.5,<5", markers = "platform_system != \"Darwin\""}
+
+[package.extras]
+snowflake = ["snowflake-connector-python (>=2.8.0)", "snowflake-snowpark-python (>=0.9.0)"]
+
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 optional = false
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+test = ["pytest", "tornado (>=4.5)", "typeguard"]
+
 [[package]]
 name = "terminado"
 version = "0.18.1"
@@ -3425,6 +4226,17 @@ dev = ["tokenizers[testing]"]
 docs = ["setuptools-rust", "sphinx", "sphinx-rtd-theme"]
 testing = ["black (==22.3)", "datasets", "numpy", "pytest", "requests", "ruff"]
 
+[[package]]
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+optional = false
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+
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 version = "2.0.1"
@@ -3658,6 +4470,17 @@ files = [
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 ]
 
+[[package]]
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+version = "2024.1"
+description = "Provider of IANA time zone data"
+optional = false
+python-versions = ">=2"
+files = [
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+]
+
 [[package]]
 name = "uri-template"
 version = "1.3.0"
@@ -3689,6 +4512,53 @@ h2 = ["h2 (>=4,<5)"]
 socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
 zstd = ["zstandard (>=0.18.0)"]
 
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 [[package]]
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 version = "0.2.13"
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+    {file = "yarl-1.9.4.tar.gz", hash = "sha256:566db86717cf8080b99b58b083b773a908ae40f06681e87e589a976faf8246bf"},
+]
+
+[package.dependencies]
+idna = ">=2.0"
+multidict = ">=4.0"
+
 [[package]]
 name = "zipp"
 version = "3.19.2"
@@ -3771,4 +4876,4 @@ test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools",
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<3.13,!=3.9.7"
-content-hash = "555e4829eaa849c175de3ee87ca3941b737533728b4196c9d224db2ee39e6e85"
+content-hash = "3f4bb2a0bfc8c717d377368f6e3fafcf7ef7d68030c6c16e0b3719dbdd9fca1f"
diff --git a/pyproject.toml b/pyproject.toml
index 2df01948..92968ab8 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "marker-pdf"
-version = "0.2.16"
+version = "0.2.17"
 description = "Convert PDF to markdown with high speed and accuracy."
 authors = ["Vik Paruchuri <github@vikas.sh>"]
 readme = "README.md"
@@ -15,6 +15,8 @@ include = [
     "convert_single.py",
     "chunk_convert.sh",
     "chunk_convert.py",
+    "marker_app.py",
+    "run_marker_app.py"
 ]
 
 [tool.poetry.dependencies]
@@ -32,7 +34,7 @@ tabulate = "^0.9.0"
 ftfy = "^6.1.1"
 texify = "^0.1.10"
 rapidfuzz = "^3.8.1"
-surya-ocr = "^0.4.15"
+surya-ocr = "^0.5.0"
 filetype = "^1.2.0"
 regex = "^2024.4.28"
 pdftext = "^0.3.10"
@@ -40,11 +42,14 @@ grpcio = "^1.63.0"
 
 [tool.poetry.group.dev.dependencies]
 jupyter = "^1.0.0"
+datasets = "^2.21.0"
+streamlit = "^1.37.1"
 
 [tool.poetry.scripts]
 marker = "convert:main"
 marker_single = "convert_single:main"
 marker_chunk_convert = "chunk_convert:main"
+marker_gui = "run_marker_app:run_app"
 
 [build-system]
 requires = ["poetry-core"]
diff --git a/run_marker_app.py b/run_marker_app.py
new file mode 100644
index 00000000..dcf6a27d
--- /dev/null
+++ b/run_marker_app.py
@@ -0,0 +1,14 @@
+import argparse
+import subprocess
+import os
+
+
+def run_app():
+    cur_dir = os.path.dirname(os.path.abspath(__file__))
+    app_path = os.path.join(cur_dir, "marker_app.py")
+    cmd = ["streamlit", "run", app_path]
+    subprocess.run(cmd, env={**os.environ, "IN_STREAMLIT": "true"})
+
+
+if __name__ == "__main__":
+    run_app()
\ No newline at end of file
diff --git a/scripts/verify_benchmark_scores.py b/scripts/verify_benchmark_scores.py
index 9d4ee25e..578d65e8 100644
--- a/scripts/verify_benchmark_scores.py
+++ b/scripts/verify_benchmark_scores.py
@@ -9,12 +9,25 @@ def verify_scores(file_path):
     multicolcnn_score = data["marker"]["files"]["multicolcnn.pdf"]["score"]
     switch_trans_score = data["marker"]["files"]["switch_trans.pdf"]["score"]
 
-    if multicolcnn_score <= 0.39 or switch_trans_score <= 0.4:
+    if multicolcnn_score <= 0.37 or switch_trans_score <= 0.4:
         raise ValueError("One or more scores are below the required threshold of 0.4")
 
 
+def verify_table_scores(file_path):
+    with open(file_path, 'r') as file:
+        data = json.load(file)
+
+    avg = sum([r["score"] for r in data]) / len(data)
+    if avg < 0.7:
+        raise ValueError("Average score is below the required threshold of 0.7")
+
+
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description="Verify benchmark scores")
     parser.add_argument("file_path", type=str, help="Path to the json file")
+    parser.add_argument("--type", type=str, help="Type of file to verify", default="marker")
     args = parser.parse_args()
-    verify_scores(args.file_path)
+    if args.type == "marker":
+        verify_scores(args.file_path)
+    elif args.type == "table":
+        verify_table_scores(args.file_path)