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 571–580 of 1209 papers

Title	Date	Tasks	Status
DuoSearch: A Novel Search Engine for Bulgarian Historical Documents	May 30, 2023	Optical Character RecognitionOptical Character Recognition (OCR)	CodeCode Available
A template-independent approach for information extraction in real estate documents	May 30, 2023	Information RetrievalNatural Language Understanding	CodeCode Available
People and Places of Historical Europe: Bootstrapping Annotation Pipeline and a New Corpus of Named Entities in Late Medieval Texts	May 26, 2023	Information Retrievalnamed-entity-recognition	—Unverified
Quantifying Character Similarity with Vision Transformers	May 24, 2023	Optical Character Recognition (OCR)	CodeCode Available
DUBLIN -- Document Understanding By Language-Image Network	May 23, 2023	Document Classificationdocument understanding	—Unverified
Measuring Intersectional Biases in Historical Documents	May 21, 2023	Optical Character RecognitionOptical Character Recognition (OCR)	CodeCode Available
TextDiffuser: Diffusion Models as Text Painters	May 18, 2023	Optical Character Recognition (OCR)	—Unverified
Sequence-to-Sequence Pre-training with Unified Modality Masking for Visual Document Understanding	May 16, 2023	Decoderdocument understanding	—Unverified
Mobile User Interface Element Detection Via Adaptively Prompt Tuning	May 16, 2023	object-detectionObject Detection	CodeCode Available
Combining OCR Models for Reading Early Modern Printed Books	May 11, 2023	Font RecognitionOptical Character Recognition (OCR)	CodeCode Available

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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