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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 1001–1025 of 1209 papers

TitleStatusHype
RealKIE: Five Novel Datasets for Enterprise Key Information Extraction—0
Real-time information retrieval from Identity cards—0
Jochre 3 and the Yiddish OCR corpusCode0
Combining OCR Models for Reading Early Modern Printed BooksCode0
Judge a Book by its Cover: Investigating Multi-Modal LLMs for Multi-Page Handwritten Document TranscriptionCode0
Scrambled text: training Language Models to correct OCR errors using synthetic dataCode0
KAP: MLLM-assisted OCR Text Enhancement for Hybrid Retrieval in Chinese Non-Narrative DocumentsCode0
SEARNN: Training RNNs with Global-Local LossesCode0
Document Rectification and Illumination Correction using a Patch-based CNNCode0
Optimal Projections for Discriminative Dictionary Learning using the JL-lemmaCode0
COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural ImagesCode0
KL3M Tokenizers: A Family of Domain-Specific and Character-Level Tokenizers for Legal, Financial, and Preprocessing ApplicationsCode0
Clustering-Based Article Identification in Historical NewspapersCode0
It Takes Two to Tango: Combining Visual and Textual Information for Detecting Duplicate Video-Based Bug ReportsCode0
Optical Character Recognition of 19th Century Classical Commentaries: the Current State of AffairsCode0
A Multi-Object Rectified Attention Network for Scene Text RecognitionCode0
Teaching Machines to Code: Neural Markup Generation with Visual AttentionCode0
An Unsupervised Model of Orthographic Variation for Historical Document TranscriptionCode0
LAREX - A semi-automatic open-source Tool for Layout Analysis and Region Extraction on Early Printed BooksCode0
Toward Advancing License Plate Super-Resolution in Real-World Scenarios: A Dataset and BenchmarkCode0
Automatic Recognition of Learning Resource Category in a Digital LibraryCode0
Investigating OCR-Sensitive Neurons to Improve Entity Recognition in Historical DocumentsCode0
Latent Tree Language ModelCode0
InstructOCR: Instruction Boosting Scene Text SpottingCode0
Infinity Parser: Layout Aware Reinforcement Learning for Scanned Document ParsingCode0
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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