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
Bengali Document Layout Analysis with Detectron2—0
An Energy Activity Dataset for Smart Homes—0
Enhancing Question Answering on Charts Through Effective Pre-training Tasks—0
Evaluating BERT-based Scientific Relation Classifiers for Scholarly Knowledge Graph Construction on Digital Library Collections—0
Diversified Hidden Markov Models for Sequential Labeling—0
Bengali Document Layout Analysis -- A YOLOV8 Based Ensembling Approach—0
Between History and Natural Language Processing: Study, Enrichment and Online Publication of French Parliamentary Debates of the Early Third Republic (1881-1899)—0
DisinfoMeme: A Multimodal Dataset for Detecting Meme Intentionally Spreading Out Disinformation—0
DocPedia: Unleashing the Power of Large Multimodal Model in the Frequency Domain for Versatile Document Understanding—0
An end-to-end Optical Character Recognition approach for ultra-low-resolution printed text images—0
DISGO: Automatic End-to-End Evaluation for Scene Text OCR—0
DocStruct: A Multimodal Method to Extract Hierarchy Structure in Document for General Form Understanding—0
DocSum: Domain-Adaptive Pre-training for Document Abstractive Summarization—0
Discriminative Dictionary Learning based on Statistical Methods—0
Document Decomposition of Bangla Printed Text—0
Benchmarking Algorithms for Automatic License Plate Recognition—0
Document Enhancement System Using Auto-encoders—0
Document Image Binarization in JPEG Compressed Domain using Dual Discriminator Generative Adversarial Networks—0
BIOfid Dataset: Publishing a German Gold Standard for Named Entity Recognition in Historical Biodiversity Literature—0
Document Layout Analysis via Dynamic Residual Feature Fusion—0
Discovering Airline-Specific Business Intelligence from Online Passenger Reviews: An Unsupervised Text Analytics Approach—0
An Investigation into Pre-Training Object-Centric Representations for Reinforcement Learning—0
DocVLM: Make Your VLM an Efficient Reader—0
DocXChain: A Powerful Open-Source Toolchain for Document Parsing and Beyond—0
Directional Global Three-part Image Decomposition—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