SOTAVerified

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 901–925 of 1209 papers

TitleStatusHype
Neural Monkey: The Current State and Beyond—0
News Deja Vu: Connecting Past and Present with Semantic Search—0
N-gram language models for massively parallel devices—0
Nonparametric modeling cash flows of insurance company—0
NOSE Augment: Fast and Effective Data Augmentation Without Searching—0
NoTeS-Bank: Benchmarking Neural Transcription and Search for Scientific Notes Understanding—0
Notes on Applicability of GPT-4 to Document Understanding—0
NusaAksara: A Multimodal and Multilingual Benchmark for Preserving Indonesian Indigenous Scripts—0
NVLM: Open Frontier-Class Multimodal LLMs—0
Object-Centric Representations Improve Policy Generalization in Robot Manipulation—0
Object Detection and Recognition of Swap-Bodies using Camera mounted on a Vehicle—0
OCR4all -- An Open-Source Tool Providing a (Semi-)Automatic OCR Workflow for Historical Printings—0
OCR accuracy improvement on document images through a novel pre-processing approach—0
OCR and Automated Translation for the Navigation of non-English Handsets: A Feasibility Study with Arabic—0
OCR and post-correction of historical Finnish texts—0
OCRAPOSE II: An OCR-based indoor positioning system using mobile phone images—0
OCR++: A Robust Framework For Information Extraction from Scholarly Articles—0
OCR, Classification & Machine Translation (OCCAM)—0
OCR Error Correction Using Character Correction and Feature-Based Word Classification—0
OCR evaluation tools for the 21st century—0
OCR for TIFF Compressed Document Images Directly in Compressed Domain Using Text segmentation and Hidden Markov Model—0
OCR Graph Features for Manipulation Detection in Documents—0
OCR Improves Machine Translation for Low-Resource Languages—0
OCR is All you need: Importing Multi-Modality into Image-based Defect Detection System—0
OCR Language Models with Custom Vocabularies—0
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All datasetsBenchmarking Chinese Text Recognition: Datasets, Baselines, and an Empirical StudyVideoDB's OCR Benchmark Public CollectionFSNS - TestI2L-140KSUTim2latex-100k

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DTrOCR 105MAccuracy (%)89.6—Unverified
2DTrOCRAccuracy (%)89.6—Unverified
3MaskOCR-LAccuracy (%)82.6—Unverified
4TransOCRAccuracy (%)72.8—Unverified
5SRNAccuracy (%)65—Unverified
6MORANAccuracy (%)64.3—Unverified
7SEEDAccuracy (%)61.2—Unverified
#ModelMetricClaimedVerifiedStatus
1GPT-4oAverage Accuracy76.22—Unverified
2Gemini-1.5 ProAverage Accuracy76.13—Unverified
3Claude-3 SonnetAverage Accuracy67.71—Unverified
4RapidOCRAverage Accuracy56.98—Unverified
5EasyOCRAverage Accuracy49.3—Unverified
#ModelMetricClaimedVerifiedStatus
1STREETSequence error27.54—Unverified
2SEESequence error22—Unverified
3AttentionOCR_Inception-resnet-v2_LocationSequence error15.8—Unverified
#ModelMetricClaimedVerifiedStatus
1I2L-NOPOOLBLEU89.09—Unverified
2I2L-STRIPSBLEU89—Unverified
#ModelMetricClaimedVerifiedStatus
1TesseractCharacter Error Rate (CER)0.08—Unverified
2EasyOCRCharacter Error Rate (CER)0.07—Unverified
#ModelMetricClaimedVerifiedStatus
1I2L-STRIPSBLEU88.86—Unverified