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 351–375 of 1209 papers

TitleStatusHype
An Efficient and Layout-Independent Automatic License Plate Recognition System Based on the YOLO detectorCode0
Implicit Language Model in LSTM for OCRCode0
Improving OCR Accuracy on Early Printed Books using Deep Convolutional NetworksCode0
Investigating OCR-Sensitive Neurons to Improve Entity Recognition in Historical DocumentsCode0
Deciphering the Underserved: Benchmarking LLM OCR for Low-Resource ScriptsCode0
DDI-100: Dataset for Text Detection and RecognitionCode0
DCQA: Document-Level Chart Question Answering towards Complex Reasoning and Common-Sense UnderstandingCode0
Historical Ink: 19th Century Latin American Spanish Newspaper Corpus with LLM OCR CorrectionCode0
Data-Driven Spelling Correction using Weighted Finite-State MethodsCode0
High-Throughput Phenotyping using Computer Vision and Machine LearningCode0
iExam: A Novel Online Exam Monitoring and Analysis System Based on Face Detection and RecognitionCode0
Data Centric Domain Adaptation for Historical Text with OCR ErrorsCode0
Handwritten Code Recognition for Pen-and-Paper CS EducationCode0
Handwritten Text Segmentation via End-to-End Learning of Convolutional Neural NetworkCode0
Analyzing Green View Index and Green View Index best path using Google Street View and deep learningCode0
Handwriting Classification for the Analysis of Art-Historical DocumentsCode0
Crossing Language Borders: A Pipeline for Indonesian Manhwa TranslationCode0
Augmented Math: Authoring AR-Based Explorable Explanations by Augmenting Static Math TextbooksCode0
HENet: Forcing a Network to Think More for Font RecognitionCode0
A Gaussian Process Upsampling Model for Improvements in Optical Character RecognitionCode0
Attention-based Extraction of Structured Information from Street View ImageryCode0
Corpus for Coreference Resolution on Scientific PapersCode0
Enhancing Cross-task Transferability of Adversarial Examples with Dispersion ReductionCode0
Object detection deep learning networks for Optical Character RecognitionCode0
An agentic system with reinforcement-learned subsystem improvements for parsing form-like documentsCode0
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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
1DTrOCR 105MAccuracy (%)89.6—Unverified
2DTrOCRAccuracy (%)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