capture_sidenotes.py

import pytesseract
import cv2


image = cv2.imread('data/sample_mgh_2.jpg')
base_image = image.copy()

gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (7, 7), 0)
thresh = cv2.threshold(
    blur, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]

# Create rectangular structuring element and dilate
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 25))
dilate = cv2.dilate(thresh, kernel, iterations=1)

# Find contours and draw rectangle
cnts = cv2.findContours(dilate, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
cnts = sorted(cnts, key=lambda x: cv2.boundingRect(x)[1])
main_text = ""
for c in cnts:
    x, y, w, h = cv2.boundingRect(c)
    if h > 200 and w > 250:
        roi = base_image[y:y+h, 0:x]
#         cv2.rectangle(image, (0, y), (x, 0 + h+20), (36,255,12), 2)

        constant = cv2.copyMakeBorder(
            roi.copy(), 30, 30, 30, 30, cv2.BORDER_CONSTANT, value=[255, 255, 255])
        ocr_result = pytesseract.image_to_string(constant)
        cv2.imwrite("temp/output.png", roi)

        print(ocr_result)
#         print (ocr_result)
# cv2.imwrite("temp/output.png", image)