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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 701–725 of 1209 papers

TitleStatusHype
Classification of Documents Extracted from Images with Optical Character Recognition Methods—0
Mixed Model OCR Training on Historical Latin Script for Out-of-the-Box Recognition and Finetuning—0
Implicit Feature Alignment: Learn to Convert Text Recognizer to Text SpotterCode1
Context-Free TextSpotter for Real-Time and Mobile End-to-End Text Detection and Recognition—0
Classification of Contract-Amendment Relationships—0
PAM: Understanding Product Images in Cross Product Category Attribute Extraction—0
End-to-End Information Extraction by Character-Level Embedding and Multi-Stage Attentional U-NetCode1
Toward Creation of Ancash Lexical Resources from OCR—0
Bangla Natural Language Processing: A Comprehensive Analysis of Classical, Machine Learning, and Deep Learning Based Methods—0
A Full-Stack Search Technique for Domain Optimized Deep Learning Accelerators—0
Empirical Error Modeling Improves Robustness of Noisy Neural Sequence LabelingCode0
Multi-Type-TD-TSR -- Extracting Tables from Document Images using a Multi-stage Pipeline for Table Detection and Table Structure Recognition: from OCR to Structured Table RepresentationsCode1
Simple Transparent Adversarial Examples—0
End-to-End Unsupervised Document Image Blind Denoising—0
STRIDE : Scene Text Recognition In-Device—0
Unknown-box Approximation to Improve Optical Character Recognition PerformanceCode1
Reciprocal Feature Learning via Explicit and Implicit Tasks in Scene Text Recognition—0
Supporting Land Reuse of Former Open Pit Mining Sites using Text Classification and Active Learning—0
TextOCR: Towards large-scale end-to-end reasoning for arbitrary-shaped scene text—0
GroupLink: An End-to-end Multitask Method for Word Grouping and Relation Extraction in Form Understanding—0
An end-to-end Optical Character Recognition approach for ultra-low-resolution printed text images—0
End-to-End Optical Character Recognition for Bengali Handwritten WordsCode0
Word-Level Alignment of Paper Documents with their Electronic Full-Text CounterpartsCode0
AT-ST: Self-Training Adaptation Strategy for OCR in Domains with Limited TranscriptionsCode1
Analyzing Green View Index and Green View Index best path using Google Street View and deep learningCode0
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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