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 701–710 of 1209 papers

Title	Date	Tasks	Status
DocBed: A Multi-Stage OCR Solution for Documents with Complex Layouts	Feb 3, 2022	ArticlesDocument Layout Analysis	—Unverified
Self-paced learning to improve text row detection in historical documents with missing labels	Jan 28, 2022	Missing LabelsOptical Character Recognition	—Unverified
An Assessment of the Impact of OCR Noise on Language Models	Jan 26, 2022	Language ModellingOptical Character Recognition	—Unverified
A Classical Approach to Handcrafted Feature Extraction Techniques for Bangla Handwritten Digit Recognition	Jan 25, 2022	Handwritten Digit RecognitionOptical Character Recognition (OCR)	—Unverified
Classroom Slide Narration System	Jan 21, 2022	Image SegmentationOptical Character Recognition	—Unverified
Legal Entity Extraction using a Pointer Generator Network	Jan 20, 2022	named-entity-recognitionNamed Entity Recognition	—Unverified
Improve Sentence Alignment by Divide-and-conquer	Jan 18, 2022	Optical Character Recognition (OCR)Sentence	—Unverified
SAFL: A Self-Attention Scene Text Recognizer with Focal Loss	Jan 1, 2022	Optical Character RecognitionOptical Character Recognition (OCR)	CodeCode Available
Intelligent Document Processing -- Methods and Tools in the real world	Dec 28, 2021	Document ClassificationOptical Character Recognition (OCR)	—Unverified
Challenging America: Modeling language in longer time scales	Dec 17, 2021	Cloze TestOptical Character Recognition (OCR)	—Unverified

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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