Optical Character Recognition
185 papers with code • 4 benchmarks • 47 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)
Benchmarks
These leaderboards are used to track progress in Optical Character Recognition
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Libraries
Use these libraries to find Optical Character Recognition models and implementations
IAM
FUNSD
ICDAR 2003
ST-VQA
TextOCR
MLe2e
UFPR-ALPR
Subtasks
Most implemented papers
An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition
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
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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
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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
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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
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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.
Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition
Recognizing irregular text in natural scene images is challenging due to the large variance in text appearance, such as curvature, orientation and distortion.
PP-OCR: A Practical Ultra Lightweight OCR System
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).
A Multi-Object Rectified Attention Network for Scene Text Recognition
It decreases the difficulty of recognition and enables the attention-based sequence recognition network to more easily read irregular text.
Robust Scene Text Recognition with Automatic Rectification
PaddlePaddle/PaddleOCR
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CVPR 2016
We show that the model is able to recognize several types of irregular text, including perspective text and curved text.
Chinese Text in the Wild
[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
