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 976–1000 of 1209 papers

TitleStatusHype
Corporate IT-support Help-Desk Process Hybrid-Automation Solution with Machine Learning Approach—0
Corpus of 19th-century Czech Texts: Problems and Solutions—0
Correction of OCR Word Segmentation Errors in Articles from the ACL Collection through Neural Machine Translation Methods—0
Corrupted but Not Broken: Understanding and Mitigating the Negative Impacts of Corrupted Data in Visual Instruction Tuning—0
CREPE: Coordinate-Aware End-to-End Document Parser—0
Crowdsourcing an OCR Gold Standard for a German and French Heritage Corpus—0
CryptoDL: Deep Neural Networks over Encrypted Data—0
CSECU\_KDE\_MA at SemEval-2020 Task 8: A Neural Attention Model for Memotion Analysis—0
DanProof: Pedagogical Spell and Grammar Checking for Danish—0
Data Cleaning for XML Electronic Dictionaries via Statistical Anomaly Detection—0
DavarOCR: A Toolbox for OCR and Multi-Modal Document Understanding—0
DECDM: Document Enhancement using Cycle-Consistent Diffusion Models—0
Decoding Anagrammed Texts Written in an Unknown Language and Script—0
Deductron -- A Recurrent Neural Network—0
Deep Learning Approach for Receipt Recognition—0
Deep learning-based NLP Data Pipeline for EHR Scanned Document Information Extraction—0
Deep Learning Based Traffic Surveillance System For Missing and Suspicious Car Detection—0
Deep Learning Based Vehicle Tracking System Using License Plate Detection And Recognition—0
Deep Reader: Information extraction from Document images via relation extraction and Natural Language—0
Deep Structured Feature Networks for Table Detection and Tabular Data Extraction from Scanned Financial Document Images—0
Delta vs. N-Gram Tracing: Evaluating the Robustness of Authorship Attribution Methods—0
Derivate-based Component-Trees for Multi-Channel Image Segmentation—0
Design and Development of a Framework For Stroke-Based Handwritten Gujarati Font Generation—0
Design and Implementation of an OCR-Powered Pipeline for Table Extraction from Invoices—0
Detecting de minimis Code-Switching in Historical German Books—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