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 1–10 of 1209 papers

TitleStatusHype
DeQA-Doc: Adapting DeQA-Score to Document Image Quality AssessmentCode0
VisionThink: Smart and Efficient Vision Language Model via Reinforcement LearningCode0
Seeing the Signs: A Survey of Edge-Deployable OCR Models for Billboard Visibility Analysis—0
A Survey on MLLM-based Visually Rich Document Understanding: Methods, Challenges, and Emerging Trends—0
Orchestrator-Agent Trust: A Modular Agentic AI Visual Classification System with Trust-Aware Orchestration and RAG-Based ReasoningCode0
Design and Implementation of an OCR-Powered Pipeline for Table Extraction from Invoices—0
TextPixs: Glyph-Conditioned Diffusion with Character-Aware Attention and OCR-Guided Supervision—0
PaddleOCR 3.0 Technical ReportCode0
Reviving Cultural Heritage: A Novel Approach for Comprehensive Historical Document RestorationCode2
DrishtiKon: Multi-Granular Visual Grounding for Text-Rich Document ImagesCode0
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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
1TesseractCharacter Error Rate (CER)0.08—Unverified
2EasyOCRCharacter Error Rate (CER)0.07—Unverified