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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 101–125 of 1209 papers

TitleStatusHype
Judge a Book by its Cover: Investigating Multi-Modal LLMs for Multi-Page Handwritten Document TranscriptionCode0
NusaAksara: A Multimodal and Multilingual Benchmark for Preserving Indonesian Indigenous Scripts—0
Detecting Offensive Memes with Social Biases in Singapore Context Using Multimodal Large Language ModelsCode0
Shakti-VLMs: Scalable Vision-Language Models for Enterprise AI—0
MultiOCR-QA: Dataset for Evaluating Robustness of LLMs in Question Answering on Multilingual OCR TextsCode0
Visual Zero-Shot E-Commerce Product Attribute Value Extraction—0
Harnessing PDF Data for Improving Japanese Large Multimodal Models—0
KITAB-Bench: A Comprehensive Multi-Domain Benchmark for Arabic OCR and Document Understanding—0
Corrupted but Not Broken: Understanding and Mitigating the Negative Impacts of Corrupted Data in Visual Instruction Tuning—0
Reading the unreadable: Creating a dataset of 19th century English newspapers using image-to-text language modelsCode0
Southern Newswire Corpus: A Large-Scale Dataset of Mid-Century Wire Articles Beyond the Front Page—0
MME-CoT: Benchmarking Chain-of-Thought in Large Multimodal Models for Reasoning Quality, Robustness, and Efficiency—0
Adapting Multilingual Embedding Models to Historical Luxembourgish—0
Benchmarking Vision-Language Models on Optical Character Recognition in Dynamic Video EnvironmentsCode1
Éclair -- Extracting Content and Layout with Integrated Reading Order for Documents—0
Towards Making Flowchart Images Machine InterpretableCode1
MME-Industry: A Cross-Industry Multimodal Evaluation Benchmark—0
Ocean-OCR: Towards General OCR Application via a Vision-Language ModelCode1
Early evidence of how LLMs outperform traditional systems on OCR/HTR tasks for historical recordsCode0
Exploring AI-based System Design for Pixel-level Protected Health Information Detection in Medical Images—0
MMDocIR: Benchmarking Multi-Modal Retrieval for Long Documents—0
Jochre 3 and the Yiddish OCR corpusCode0
Comparative analysis of optical character recognition methods for Sámi texts from the National Library of NorwayCode0
MathReader : Text-to-Speech for Mathematical DocumentsCode1
Centurio: On Drivers of Multilingual Ability of Large Vision-Language ModelCode0
Show:102550
← PrevPage 5 of 49Next →
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