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 376–400 of 1209 papers

TitleStatusHype
MMDocBench: Benchmarking Large Vision-Language Models for Fine-Grained Visual Document Understanding—0
Towards Visual Text Design Transfer Across Languages—0
Reference-Based Post-OCR Processing with LLM for Diacritic Languages—0
Harnessing Webpage UIs for Text-Rich Visual Understanding—0
LEGAL-UQA: A Low-Resource Urdu-English Dataset for Legal Question AnsweringCode0
Enhancing Assamese NLP Capabilities: Introducing a Centralized Dataset RepositoryCode0
Comparison of Image Preprocessing Techniques for Vehicle License Plate Recognition Using OCR: Performance and Accuracy Evaluation—0
ReLayout: Towards Real-World Document Understanding via Layout-enhanced Pre-training—0
TextMaster: Universal Controllable Text Edit—0
MIRAGE: Multimodal Identification and Recognition of Annotations in Indian General Prescriptions—0
Unraveling Movie Genres through Cross-Attention Fusion of Bi-Modal Synergy of Poster—0
Mero Nagarikta: Advanced Nepali Citizenship Data Extractor with Deep Learning-Powered Text Detection and OCR—0
Automated Quality Control System for Canned Tuna Production using Artificial Vision—0
Transformers Utilization in Chart Understanding: A Review of Recent Advances & Future Trends—0
Khattat: Enhancing Readability and Concept Representation of Semantic Typography—0
World to Code: Multi-modal Data Generation via Self-Instructed Compositional Captioning and FilteringCode0
JaPOC: Japanese Post-OCR Correction Benchmark using Vouchers—0
MM1.5: Methods, Analysis & Insights from Multimodal LLM Fine-tuning—0
Scrambled text: training Language Models to correct OCR errors using synthetic dataCode0
See then Tell: Enhancing Key Information Extraction with Vision Grounding—0
CodeSCAN: ScreenCast ANalysis for Video Programming Tutorials—0
JoyType: A Robust Design for Multilingual Visual Text Creation—0
MaViLS, a Benchmark Dataset for Video-to-Slide Alignment, Assessing Baseline Accuracy with a Multimodal Alignment Algorithm Leveraging Speech, OCR, and Visual FeaturesCode0
Investigating OCR-Sensitive Neurons to Improve Entity Recognition in Historical DocumentsCode0
@Bench: Benchmarking Vision-Language Models for Human-centered Assistive Technology—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