Optical Character Recognition
114 papers with code • 1 benchmarks • 23 datasets
Benchmarks
| Trend | Dataset | Best Model | Paper Title | Paper | Code | Compare |
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Datasets
IAM
ICDAR 2003
FUNSD
MLe2e
Kannada-MNIST
Chinese Text in the Wild
Subtasks
Greatest papers with code
Attention-based Extraction of Structured Information from Street View Imagery
We present a neural network model - based on CNNs, RNNs and a novel attention mechanism - which achieves 84. 2% accuracy on the challenging French Street Name Signs (FSNS) dataset, significantly outperforming the previous state of the art (Smith'16), which achieved 72. 46%.
Ranked #1 on
Optical Character Recognition
on FSNS - Test
Adapting the Tesseract Open Source OCR Engine for Multilingual OCR
We describe efforts to adapt the Tesseract open source OCR engine for multiple scripts and languages.
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).
Learning Transferable Visual Models From Natural Language Supervision
openai/CLIP
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State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories.
Ranked #1 on
Zero-Shot Transfer Image Classification
on aYahoo
End-to-End Interpretation of the French Street Name Signs Dataset
We introduce the French Street Name Signs (FSNS) Dataset consisting of more than a million images of street name signs cropped from Google Street View images of France.
Ranked #3 on
Optical Character Recognition
on FSNS - Test
COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural Images
The goal of COCO-Text is to advance state-of-the-art in text detection and recognition in natural images.
Segmentation Transformer: Object-Contextual Representations for Semantic Segmentation
HRNet/HRNet-Semantic-Segmentation
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ECCV 2020
We empirically demonstrate that the proposed approach achieves competitive performance on various challenging semantic segmentation benchmarks: Cityscapes, ADE20K, LIP, PASCAL-Context, and COCO-Stuff.
Ranked #2 on
Semantic Segmentation
on LIP val
License Plate Detection and Recognition in Unconstrained Scenarios
sergiomsilva/alpr-unconstrained
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ECCV 2018
Despite the large number of both commercial and academic methods for Automatic License Plate Recognition (ALPR), most existing approaches are focused on a specific license plate (LP) region (e. g. European, US, Brazilian, Taiwanese, etc.
Ranked #2 on
License Plate Recognition
on AOLP-RP
Image-to-Markup Generation with Coarse-to-Fine Attention
da03/Attention-OCR
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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.