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 51–75 of 1209 papers

TitleStatusHype
Let's Fuse Step by Step: A Generative Fusion Decoding Algorithm with LLMs for Multi-modal Text RecognitionCode2
DTrOCR: Decoder-only Transformer for Optical Character RecognitionCode2
LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image UnderstandingCode2
An Empirical Study of Scaling Law for Scene Text RecognitionCode2
GUICourse: From General Vision Language Models to Versatile GUI AgentsCode2
IMKGA-SM: Interpretable Multimodal Knowledge Graph Answer Prediction via Sequence ModelingCode2
GIT: A Generative Image-to-text Transformer for Vision and LanguageCode2
Delivering Document Conversion as a Cloud Service with High Throughput and ResponsivenessCode2
General Detection-based Text Line RecognitionCode2
GlyphControl: Glyph Conditional Control for Visual Text GenerationCode2
MouSi: Poly-Visual-Expert Vision-Language ModelsCode2
BLIVA: A Simple Multimodal LLM for Better Handling of Text-Rich Visual QuestionsCode2
FigStep: Jailbreaking Large Vision-Language Models via Typographic Visual PromptsCode1
BROS: A Pre-trained Language Model Focusing on Text and Layout for Better Key Information Extraction from DocumentsCode1
FUNSD: A Dataset for Form Understanding in Noisy Scanned DocumentsCode1
FAWA: Fast Adversarial Watermark Attack on Optical Character Recognition (OCR) SystemsCode1
FlowLearn: Evaluating Large Vision-Language Models on Flowchart UnderstandingCode1
Benchmarking Vision-Language Models on Optical Character Recognition in Dynamic Video EnvironmentsCode1
A Benchmark and Dataset for Post-OCR text correction in SanskritCode1
Exploring Cross-Image Pixel Contrast for Semantic SegmentationCode1
Enhancing License Plate Super-Resolution: A Layout-Aware and Character-Driven ApproachCode1
Adapting OCR with limited supervisionCode1
Exploring Better Text Image Translation with Multimodal CodebookCode1
Exploring OCR Capabilities of GPT-4V(ision) : A Quantitative and In-depth EvaluationCode1
EAST: An Efficient and Accurate Scene Text DetectorCode1
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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