Optical Character Recognition (OCR)

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)

Papers

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Showing 201–210 of 1209 papers

Title	Date	Tasks	Status	Hype
Fused Text Recogniser and Deep Embeddings Improve Word Recognition and Retrieval	Jul 1, 2020	Optical Character Recognition (OCR)Retrieval	CodeCode Available	1
Improving accuracy and speeding up Document Image Classification through parallel systems	Jun 16, 2020	Document Classificationdocument-image-classification	CodeCode Available	1
CLEval: Character-Level Evaluation for Text Detection and Recognition Tasks	Jun 11, 2020	Optical Character Recognition (OCR)Text Detection	CodeCode Available	1
Fully Unsupervised Diversity Denoising with Convolutional Variational Autoencoders	Jun 10, 2020	Cell SegmentationDenoising	CodeCode Available	1
Structured Multimodal Attentions for TextVQA	Jun 1, 2020	Graph AttentionOptical Character Recognition (OCR)	CodeCode Available	1
SEED: Semantics Enhanced Encoder-Decoder Framework for Scene Text Recognition	May 22, 2020	DecoderOptical Character Recognition (OCR)	CodeCode Available	1
NAT: Noise-Aware Training for Robust Neural Sequence Labeling	May 14, 2020	Data Augmentationnamed-entity-recognition	CodeCode Available	1
Large Scale Font Independent Urdu Text Recognition System	May 14, 2020	Incremental LearningOptical Character Recognition (OCR)	CodeCode Available	1
The Newspaper Navigator Dataset: Extracting And Analyzing Visual Content from 16 Million Historic Newspaper Pages in Chronicling America	May 4, 2020	Optical Character Recognition (OCR)	CodeCode Available	1
PICK: Processing Key Information Extraction from Documents using Improved Graph Learning-Convolutional Networks	Apr 16, 2020	Graph LearningKey Information Extraction	CodeCode Available	1

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All datasets Benchmarking Chinese Text Recognition: Datasets, Baselines, and an Empirical Study VideoDB's OCR Benchmark Public Collection FSNS - Test I2L-140K SUT im2latex-100k

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	DTrOCR	Accuracy (%)	89.6	—	Unverified
2	DTrOCR 105M	Accuracy (%)	89.6	—	Unverified
3	MaskOCR-L	Accuracy (%)	82.6	—	Unverified
4	TransOCR	Accuracy (%)	72.8	—	Unverified
5	SRN	Accuracy (%)	65	—	Unverified
6	MORAN	Accuracy (%)	64.3	—	Unverified
7	SEED	Accuracy (%)	61.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	GPT-4o	Average Accuracy	76.22	—	Unverified
2	Gemini-1.5 Pro	Average Accuracy	76.13	—	Unverified
3	Claude-3 Sonnet	Average Accuracy	67.71	—	Unverified
4	RapidOCR	Average Accuracy	56.98	—	Unverified
5	EasyOCR	Average Accuracy	49.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	STREET	Sequence error	27.54	—	Unverified
2	SEE	Sequence error	22	—	Unverified
3	AttentionOCR_Inception-resnet-v2_Location	Sequence error	15.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	I2L-NOPOOL	BLEU	89.09	—	Unverified
2	I2L-STRIPS	BLEU	89	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Tesseract	Character Error Rate (CER)	0.08	—	Unverified
2	EasyOCR	Character Error Rate (CER)	0.07	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	I2L-STRIPS	BLEU	88.86	—	Unverified