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 401–425 of 1209 papers

TitleStatusHype
Efficient few-shot learning for pixel-precise handwritten document layout analysis—0
Efficient, Lexicon-Free OCR using Deep Learning—0
A Novel Pipeline for Improving Optical Character Recognition through Post-processing Using Natural Language Processing—0
Efficient Media Retrieval from Non-Cooperative Queries—0
BART for Post-Correction of OCR Newspaper Text—0
Building OCR/NER Test Collections—0
Development of a New Image-to-text Conversion System for Pashto, Farsi and Traditional Chinese—0
Detection of Text Reuse in French Medical Corpora—0
Bangla Text Recognition from Video Sequence: A New Focus—0
A Novel Transfer Learning Approach upon Hindi, Arabic, and Bangla Numerals using Convolutional Neural Networks—0
Embedding Similarity Guided License Plate Super Resolution—0
A Hybrid Swarm and Gravitation based feature selection algorithm for Handwritten Indic Script Classification problem—0
Endangered Data for Endangered Languages: Digitizing Print dictionaries—0
An End-to-End Khmer Optical Character Recognition using Sequence-to-Sequence with Attention—0
An Ensemble of Neural Networks for Non-Linear Segmentation of Overlapped Cursive Script—0
Fetch-A-Set: A Large-Scale OCR-Free Benchmark for Historical Document Retrieval—0
CalliReader: Contextualizing Chinese Calligraphy via an Embedding-Aligned Vision-Language Model—0
End-to-End Piece-Wise Unwarping of Document Images—0
Detection Masking for Improved OCR on Noisy Documents—0
Engineering RAG Systems for Real-World Applications: Design, Development, and Evaluation—0
Enhancement of Bengali OCR by Specialized Models and Advanced Techniques for Diverse Document Types—0
Enhancement of text recognition for hanja handwritten documents of Ancient Korea—0
Bangla Natural Language Processing: A Comprehensive Analysis of Classical, Machine Learning, and Deep Learning Based Methods—0
D\'etection d'erreurs dans des transcriptions OCR de documents historiques par r\'eseaux de neurones r\'ecurrents multi-niveau (Combining character level and word level RNNs for post-OCR error detection)—0
Bambara and Maninka Manding Languages Written Corpora Project (``Projet des corpus \'ecrits des langues manding : le bambara, le maninka'') [in French]—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
1DTrOCRAccuracy (%)89.6—Unverified
2DTrOCR 105MAccuracy (%)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