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 176–200 of 1209 papers

TitleStatusHype
DriveThru: a Document Extraction Platform and Benchmark Datasets for Indonesian Local Language ArchivesCode0
Is Cognition consistent with Perception? Assessing and Mitigating Multimodal Knowledge Conflicts in Document Understanding—0
Veri-Car: Towards Open-world Vehicle Information Retrieval—0
Hierarchical Visual Feature Aggregation for OCR-Free Document Understanding—0
NeKo: Toward Post Recognition Generative Correction Large Language Models with Task-Oriented Experts—0
M3DocRAG: Multi-modal Retrieval is What You Need for Multi-page Multi-document Understanding—0
TAP-VL: Text Layout-Aware Pre-training for Enriched Vision-Language Models—0
Out-of-Distribution Recovery with Object-Centric Keypoint Inverse Policy for Visuomotor Imitation Learning—0
HIP: Hierarchical Point Modeling and Pre-training for Visual Information Extraction—0
Handwriting Recognition in Historical Documents with Multimodal LLM—0
Toxicity of the Commons: Curating Open-Source Pre-Training DataCode1
Are VLMs Really BlindCode0
Structured Analysis and Comparison of Alphabets in Historical Handwritten Ciphers—0
MMDocBench: Benchmarking Large Vision-Language Models for Fine-Grained Visual Document Understanding—0
Towards Visual Text Design Transfer Across Languages—0
Harnessing Webpage UIs for Text-Rich Visual Understanding—0
Reference-Based Post-OCR Processing with LLM for Diacritic Languages—0
LEGAL-UQA: A Low-Resource Urdu-English Dataset for Legal Question AnsweringCode0
Comparison of Image Preprocessing Techniques for Vehicle License Plate Recognition Using OCR: Performance and Accuracy Evaluation—0
Enhancing Assamese NLP Capabilities: Introducing a Centralized Dataset RepositoryCode0
ReLayout: Towards Real-World Document Understanding via Layout-enhanced Pre-training—0
TextMaster: Universal Controllable Text Edit—0
Stratified Domain Adaptation: A Progressive Self-Training Approach for Scene Text RecognitionCode1
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
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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