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 141–150 of 1209 papers

Title	Date	Tasks	Status	Hype	Score
Fused Text Recogniser and Deep Embeddings Improve Word Recognition and Retrieval	Jul 1, 2020	Optical Character Recognition (OCR)Retrieval	CodeCode Available	1	5
From Text to Pixel: Advancing Long-Context Understanding in MLLMs	May 23, 2024	Language ModelingLanguage Modelling	CodeCode Available	1	5
FuseCap: Leveraging Large Language Models for Enriched Fused Image Captions	May 28, 2023	AttributeImage Captioning	CodeCode Available	1	5
Generating Synthetic Handwritten Historical Documents With OCR Constrained GANs	Mar 15, 2021	Optical Character Recognition (OCR)Synthetic Data Generation	CodeCode Available	1	5
BROS: A Pre-trained Language Model Focusing on Text and Layout for Better Key Information Extraction from Documents	Aug 10, 2021	Key Information ExtractionLanguage Modeling	CodeCode Available	1	5
German Parliamentary Corpus (GerParCor)	Apr 21, 2022	Optical Character Recognition (OCR)	CodeCode Available	1	5
A Multiplexed Network for End-to-End, Multilingual OCR	Mar 29, 2021	Optical Character Recognition (OCR)Text Detection	CodeCode Available	1	5
A Two-Step Approach for Automatic OCR Post-Correction	Dec 1, 2020	Optical Character RecognitionOptical Character Recognition (OCR)	CodeCode Available	1	5
ChartReader: A Unified Framework for Chart Derendering and Comprehension without Heuristic Rules	Apr 5, 2023	Chart UnderstandingDerendering	CodeCode Available	1	5
FlowLearn: Evaluating Large Vision-Language Models on Flowchart Understanding	Jul 6, 2024	Optical Character Recognition (OCR)Visual Question Answering (VQA)	CodeCode Available	1	5

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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 105M	Accuracy (%)	89.6	—	Unverified
2	DTrOCR	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