Optical Character Recognition (OCR)

241 papers with code • 4 benchmarks • 51 datasets

Optical Character Recognition or Optical Character Reader (OCR) is the electronic or mechanical conversion of images of typed, handwritten or printed text into machine-encoded text, whether from a scanned document, a photo of a document, a scene-photo (for example the text on signs and billboards in a landscape photo, license plates in cars...) or from subtitle text superimposed on an image (for example: from a television broadcast)


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Most implemented papers

An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition

sartaj0/TextRecognition-Pytorch 21 Jul 2015

In this paper, we investigate the problem of scene text recognition, which is among the most important and challenging tasks in image-based sequence recognition.

EAST: An Efficient and Accurate Scene Text Detector

PaddlePaddle/PaddleOCR CVPR 2017

Previous approaches for scene text detection have already achieved promising performances across various benchmarks.

Image-to-Markup Generation with Coarse-to-Fine Attention

harvardnlp/im2markup ICML 2017

We present a neural encoder-decoder model to convert images into presentational markup based on a scalable coarse-to-fine attention mechanism.

Shape Robust Text Detection with Progressive Scale Expansion Network

PaddlePaddle/PaddleOCR CVPR 2019

Due to the fact that there are large geometrical margins among the minimal scale kernels, our method is effective to split the close text instances, making it easier to use segmentation-based methods to detect arbitrary-shaped text instances.

Real-time Scene Text Detection with Differentiable Binarization

MhLiao/DB 20 Nov 2019

Recently, segmentation-based methods are quite popular in scene text detection, as the segmentation results can more accurately describe scene text of various shapes such as curve text.

PP-OCR: A Practical Ultra Lightweight OCR System

PaddlePaddle/PaddleOCR 21 Sep 2020

Meanwhile, several pre-trained models for the Chinese and English recognition are released, including a text detector (97K images are used), a direction classifier (600K images are used) as well as a text recognizer (17. 9M images are used).

Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition

PaddlePaddle/PaddleOCR 2 Nov 2018

Recognizing irregular text in natural scene images is challenging due to the large variance in text appearance, such as curvature, orientation and distortion.

A Multi-Object Rectified Attention Network for Scene Text Recognition

Canjie-Luo/MORAN_v2 10 Jan 2019

It decreases the difficulty of recognition and enables the attention-based sequence recognition network to more easily read irregular text.

Image-based table recognition: data, model, and evaluation

ibm-aur-nlp/PubTabNet ECCV 2020

In addition, we propose a new Tree-Edit-Distance-based Similarity (TEDS) metric for table recognition, which more appropriately captures multi-hop cell misalignment and OCR errors than the pre-established metric.

Chinese Text in the Wild

xiaofengShi/CHINESE-OCR 28 Feb 2018

[python3. 6] 运用tf实现自然场景文字检测, keras/pytorch实现ctpn+crnn+ctc实现不定长场景文字OCR识别