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 226–250 of 1209 papers

TitleStatusHype
MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding BenchmarkCode4
General OCR Theory: Towards OCR-2.0 via a Unified End-to-end ModelCode9
Post-OCR Text Correction for Bulgarian Historical DocumentsCode0
CLOCR-C: Context Leveraging OCR Correction with Pre-trained Language ModelsCode0
ChartEye: A Deep Learning Framework for Chart Information Extraction—0
Can Visual Language Models Replace OCR-Based Visual Question Answering Pipelines in Production? A Case Study in Retail—0
DocLayLLM: An Efficient and Effective Multi-modal Extension of Large Language Models for Text-rich Document UnderstandingCode1
Platypus: A Generalized Specialist Model for Reading Text in Various Forms—0
FastTextSpotter: A High-Efficiency Transformer for Multilingual Scene Text SpottingCode0
A Permuted Autoregressive Approach to Word-Level Recognition for Urdu Digital Text—0
Knowledge Discovery in Optical Music Recognition: Enhancing Information Retrieval with Instance Segmentation—0
Enhancing License Plate Super-Resolution: A Layout-Aware and Character-Driven ApproachCode1
MMR: Evaluating Reading Ability of Large Multimodal Models—0
Ancient but Digitized: Developing Handwritten Optical Character Recognition for East Syriac Script Through Creating KHAMIS Dataset—0
Vintern-1B: An Efficient Multimodal Large Language Model for Vietnamese—0
Large Language Models for Page Stream Segmentation—0
ChemVLM: Exploring the Power of Multimodal Large Language Models in Chemistry AreaCode2
SWIFT:A Scalable lightWeight Infrastructure for Fine-TuningCode11
Handwritten Code Recognition for Pen-and-Paper CS EducationCode0
Advancing Post-OCR Correction: A Comparative Study of Synthetic DataCode0
MiniCPM-V: A GPT-4V Level MLLM on Your PhoneCode12
Focus, Distinguish, and Prompt: Unleashing CLIP for Efficient and Flexible Scene Text RetrievalCode1
MM-Vet v2: A Challenging Benchmark to Evaluate Large Multimodal Models for Integrated CapabilitiesCode3
PIXELMOD: Improving Soft Moderation of Visual Misleading Information on TwitterCode0
Image-text matching for large-scale book collectionsCode1
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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