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 701–750 of 1209 papers

TitleStatusHype
DocBed: A Multi-Stage OCR Solution for Documents with Complex Layouts—0
Self-paced learning to improve text row detection in historical documents with missing labels—0
An Assessment of the Impact of OCR Noise on Language Models—0
A Classical Approach to Handcrafted Feature Extraction Techniques for Bangla Handwritten Digit Recognition—0
Classroom Slide Narration System—0
Legal Entity Extraction using a Pointer Generator Network—0
Improve Sentence Alignment by Divide-and-conquer—0
SAFL: A Self-Attention Scene Text Recognizer with Focal LossCode0
Intelligent Document Processing -- Methods and Tools in the real world—0
Challenging America: Modeling language in longer time scales—0
Lesan -- Machine Translation for Low Resource Languages—0
Tracing Text Provenance via Context-Aware Lexical Substitution—0
Modelling Lips-State Detection Using CNN for Non-Verbal Communications—0
A Survey on Deep learning based Document Image Enhancement—0
On-Device Spatial Attention based Sequence Learning Approach for Scene Text Script Identification—0
Transferring Modern Named Entity Recognition to the Historical Domain: How to Take the Step?—0
Image preprocessing and modified adaptive thresholding for improving OCR—0
Ice hockey player identification via transformers and weakly supervised learning—0
Discriminative Dictionary Learning based on Statistical Methods—0
Handwritten Digit Recognition Using Improved Bounding Box Recognition Technique—0
SpellBERT: A Lightweight Pretrained Model for Chinese Spelling Check—0
Unsupervised Multi-View Post-OCR Error Correction With Language Models—0
Named Entity Recognition in Historic Legal Text: A Transformer and State Machine Ensemble Method—0
BART for Post-Correction of OCR Newspaper Text—0
Ultra Light OCR Competition Technical Report—0
Cleaning Dirty Books: Post-OCR Processing for Previously Scanned TextsCode0
HENet: Forcing a Network to Think More for Font RecognitionCode0
Learning UI Navigation through Demonstrations composed of Macro Actions—0
Optical Character Recognition of 19th Century Classical Commentaries: the Current State of AffairsCode0
Robustness Evaluation of Transformer-based Form Field Extractors via Form Attacks—0
Asking questions on handwritten document collections—0
A Proposal of Automatic Error Correction in Text—0
Deep learning-based NLP Data Pipeline for EHR Scanned Document Information Extraction—0
Adapting the Tesseract Open-Source OCR Engine for Tamil and Sinhala Legacy Fonts and Creating a Parallel Corpus for Tamil-Sinhala-EnglishCode0
OCR Processing of Swedish Historical Newspapers Using Deep Hybrid CNN–LSTM Networks—0
A Novel Machine Learning Based Approach for Post-OCR Error Detection—0
A Multimodal Framework for Video Ads Understanding—0
LayoutReader: Pre-training of Text and Layout for Reading Order Detection—0
EKTVQA: Generalized use of External Knowledge to empower Scene Text in Text-VQA—0
Localize, Group, and Select: Boosting Text-VQA by Scene Text Modeling—0
Real-time Bangla License Plate Recognition System for Low Resource Video-based Applications—0
VisBuddy -- A Smart Wearable Assistant for the Visually Challenged—0
MMOCR: A Comprehensive Toolbox for Text Detection, Recognition and Understanding—0
The Corpora They Are a-Changing: a Case Study in Italian Newspapers—0
MinD at SemEval-2021 Task 6: Propaganda Detection using Transfer Learning and Multimodal Fusion—0
Scene Text recognition with Full Normalization—0
Memes in the Wild: Assessing the Generalizability of the Hateful Memes Challenge Dataset—0
Data Centric Domain Adaptation for Historical Text with OCR ErrorsCode0
Automatic Metadata Extraction Incorporating Visual Features from Scanned Electronic Theses and DissertationsCode0
SAR-Net: Shape Alignment and Recovery Network for Category-level 6D Object Pose and Size Estimation—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