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 851–875 of 1209 papers

TitleStatusHype
Making the V in Text-VQA Matter—0
MaskOCR: Text Recognition with Masked Encoder-Decoder Pretraining—0
MataDoc: Margin and Text Aware Document Dewarping for Arbitrary Boundary—0
MathWriting: A Dataset For Handwritten Mathematical Expression Recognition—0
Matics Software Suite: New Tools for Evaluation and Data Exploration—0
MatriVasha: A Multipurpose Comprehensive Database for Bangla Handwritten Compound Characters—0
Measuring Contextual Fitness Using Error Contexts Extracted from the Wikipedia Revision History—0
Measuring Innovation in Speech and Language Processing Publications.—0
Measuring Lexical Quality of a Historical Finnish Newspaper Collection ― Analysis of Garbled OCR Data with Basic Language Technology Tools and Means—0
Membership Model Inversion Attacks for Deep Networks—0
Meme Sentiment Analysis Enhanced with Multimodal Spatial Encoding and Facial Embedding—0
Memes in the Wild: Assessing the Generalizability of the Hateful Memes Challenge Dataset—0
MenuAI: Restaurant Food Recommendation System via a Transformer-based Deep Learning Model—0
Mero Nagarikta: Advanced Nepali Citizenship Data Extractor with Deep Learning-Powered Text Detection and OCR—0
MinD at SemEval-2021 Task 6: Propaganda Detection using Transfer Learning and Multimodal Fusion—0
Mind the Gap: Analyzing Lacunae with Transformer-Based Transcription—0
Supporting Land Reuse of Former Open Pit Mining Sites using Text Classification and Active Learning—0
MIRAGE: Multimodal Identification and Recognition of Annotations in Indian General Prescriptions—0
Mitigating Noisy Inputs for Question Answering—0
Mixed Model OCR Training on Historical Latin Script for Out-of-the-Box Recognition and Finetuning—0
MM1.5: Methods, Analysis & Insights from Multimodal LLM Fine-tuning—0
MMDocBench: Benchmarking Large Vision-Language Models for Fine-Grained Visual Document Understanding—0
MMDocIR: Benchmarking Multi-Modal Retrieval for Long Documents—0
MME-CoT: Benchmarking Chain-of-Thought in Large Multimodal Models for Reasoning Quality, Robustness, and Efficiency—0
MME-Industry: A Cross-Industry Multimodal Evaluation Benchmark—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