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

Showing 5175 of 1209 papers

TitleStatusHype
An Approach for Air Drawing Using Background Subtraction and Contour ExtractionCode2
LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image UnderstandingCode2
MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual ContextsCode2
DTrOCR: Decoder-only Transformer for Optical Character RecognitionCode2
MMLongBench-Doc: Benchmarking Long-context Document Understanding with VisualizationsCode2
IMKGA-SM: Interpretable Multimodal Knowledge Graph Answer Prediction via Sequence ModelingCode2
GUICourse: From General Vision Language Models to Versatile GUI AgentsCode2
Let's Fuse Step by Step: A Generative Fusion Decoding Algorithm with LLMs for Multi-modal Text RecognitionCode2
Arabic-Nougat: Fine-Tuning Vision Transformers for Arabic OCR and Markdown ExtractionCode2
GIT: A Generative Image-to-text Transformer for Vision and LanguageCode2
General Detection-based Text Line RecognitionCode2
GlyphControl: Glyph Conditional Control for Visual Text GenerationCode2
FigStep: Jailbreaking Large Vision-Language Models via Typographic Visual PromptsCode1
FAWA: Fast Adversarial Watermark Attack on Optical Character Recognition (OCR) SystemsCode1
FUNSD: A Dataset for Form Understanding in Noisy Scanned DocumentsCode1
Exploring OCR Capabilities of GPT-4V(ision) : A Quantitative and In-depth EvaluationCode1
FlowLearn: Evaluating Large Vision-Language Models on Flowchart UnderstandingCode1
Enhancing License Plate Super-Resolution: A Layout-Aware and Character-Driven ApproachCode1
A Benchmark and Dataset for Post-OCR text correction in SanskritCode1
Exploring Better Text Image Translation with Multimodal CodebookCode1
Efficient OCR for Building a Diverse Digital HistoryCode1
Adapting OCR with limited supervisionCode1
End-to-End Information Extraction by Character-Level Embedding and Multi-Stage Attentional U-NetCode1
Exploring Cross-Image Pixel Contrast for Semantic SegmentationCode1
DSG: An End-to-End Document Structure GeneratorCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DTrOCR 105MAccuracy (%)89.6Unverified
2DTrOCRAccuracy (%)89.6Unverified
3MaskOCR-LAccuracy (%)82.6Unverified
4TransOCRAccuracy (%)72.8Unverified
5SRNAccuracy (%)65Unverified
6MORANAccuracy (%)64.3Unverified
7SEEDAccuracy (%)61.2Unverified
#ModelMetricClaimedVerifiedStatus
1GPT-4oAverage Accuracy76.22Unverified
2Gemini-1.5 ProAverage Accuracy76.13Unverified
3Claude-3 SonnetAverage Accuracy67.71Unverified
4RapidOCRAverage Accuracy56.98Unverified
5EasyOCRAverage Accuracy49.3Unverified
#ModelMetricClaimedVerifiedStatus
1STREETSequence error27.54Unverified
2SEESequence error22Unverified
3AttentionOCR_Inception-resnet-v2_LocationSequence error15.8Unverified
#ModelMetricClaimedVerifiedStatus
1I2L-NOPOOLBLEU89.09Unverified
2I2L-STRIPSBLEU89Unverified
#ModelMetricClaimedVerifiedStatus
1TesseractCharacter Error Rate (CER)0.08Unverified
2EasyOCRCharacter Error Rate (CER)0.07Unverified
#ModelMetricClaimedVerifiedStatus
1I2L-STRIPSBLEU88.86Unverified