This project uses deep learning and NLP to classify songs by sentiment and genre through combined audio and text analysis. It processes lyrics and audio features, integrating both in a neural network for enhanced accuracy, providing a structured pipeline for data loading, training, and visualization.

This project offers an efficient method for identifying and recognizing handwritten text from images. Using a Convolutional Recurrent Neural Network (CRNN) for Optical Character Recognition (OCR), it effectively extracts text from images, aiding in the digitization of handwritten documents and automated text extraction.

Raw Audio End-to-End Deep Learning Architectures for Sound Event Detection

Convolutional Recurrent Neural Networks(CRNN) for Scene Text Recognition

Use Convolutional Recurrent Neural Network to recognize the Handwritten line text image without pre segmentation into words or characters. Use CTC loss Function to train.

Tuning, training, and transfer learning CRNN models for handwritten text words.

RPA(Robotic Process Automation) UiPath 實戰開發範例

Tensorflow practice

Implementation of the 2019 paper 'Autoregressive Convolutional Recurrent Neural Network for Univariate and Multivariate Time Series Prediction'

Convolutional Recurrent Neural Network (CRNN) for reading Captchas(驗證碼)

CRNN implementation with TensorFlow 2

My implementations of different deep learning architectures for text recognition with TensorFlow 2.

This repository contains code for Scene Text OCR following the famous CRNN model. The detection part is handled by EAST and the recognition is done by CRNN.

ctc decoder

TF2 implementation of Text recognition CRNN