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 831–840 of 1209 papers

Title	Date	Tasks	Status
Legal Entity Extraction using a Pointer Generator Network	Jan 20, 2022	named-entity-recognitionNamed Entity Recognition	—Unverified
Lesan -- Machine Translation for Low Resource Languages	Dec 15, 2021	ArticlesMachine Translation	—Unverified
Leveraging Statistical Transliteration for Dictionary-Based English-Bengali CLIR of OCR`d Text	Dec 1, 2012	Information RetrievalOptical Character Recognition (OCR)	—Unverified
Leveraging Text Repetitions and Denoising Autoencoders in OCR Post-correction	Jun 26, 2019	DenoisingOptical Character Recognition (OCR)	—Unverified
License Plate Recognition System Based on Color Coding Of License Plates	Jun 8, 2015	License Plate RecognitionOptical Character Recognition	—Unverified
Linear-Time Sequence Classification using Restricted Boltzmann Machines	Oct 6, 2017	ChunkingClassification	—Unverified
Linguistic Resources for Handwriting Recognition and Translation Evaluation	May 1, 2012	Document ClassificationHandwriting Recognition	—Unverified
Linking Representations with Multimodal Contrastive Learning	Apr 7, 2023	Contrastive LearningOptical Character Recognition	—Unverified
Lipi Gnani - A Versatile OCR for Documents in any Language Printed in Kannada Script	Jan 2, 2019	BinarizationOptical Character Recognition (OCR)	—Unverified
Localize, Group, and Select: Boosting Text-VQA by Scene Text Modeling	Aug 20, 2021	Data AblationOptical Character Recognition	—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