Ready-to-use OCR with 40+ languages supported including Chinese, Japanese, Korean and Thai

Text recognition (optical character recognition) with deep learning methods.

TextBoxes++: A Single-Shot Oriented Scene Text Detector

Recognizing cropped text in natural images.

This repository provides train＆test code, dataset, det.&rec. annotation, evaluation script, annotation tool, and ranking.

Total Text Dataset. It consists of 1555 images with more than 3 different text orientations: Horizontal, Multi-Oriented, and Curved, one of a kind.

MORAN: A Multi-Object Rectified Attention Network for Scene Text Recognition

ASTER in Pytorch

Geometric Augmentation for Text Image

Single Shot Text Detector with Regional Attention

Implementation of EAST scene text detector in Keras

A novel region proposal network for more general object detection ( including scene text detection ).

TextField: Learning A Deep Direction Field for Irregular Scene Text Detection (TIP 2019)

Convolutional recurrent neural network for scene text recognition or OCR in Keras

This is a c++ project deploying a deep scene text reading pipeline with tensorflow. It reads text from natural scene images. It uses frozen tensorflow graphs. The detector detect scene text locations. The recognizer reads word from each detected bounding box.

A curated list of papers and resources for scene text detection and recognition

This project modify tensorflow object detection api code to predict oriented bounding boxes. It can be used for scene text detection.

This repository contains the codes and instructions to use the trained models for all the four datasets described in the paper : 'Script Identification in Natural Scene Image and Video Frame using Attention based Convolutional-LSTM Network'

A Word Spotting Method in Scene Images based on Character Recognition

Pytorch implementation for pixel-wise scene text segmentation based on DeepLabV3+

Code for generating synthetic text images in Indic languages. Based on "Synthetic Data for Text Localisation in Natural Images", Ankush Gupta, Andrea Vedaldi, Andrew Zisserman, CVPR 2016.