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 601–625 of 1209 papers

TitleStatusHype
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
D\'etection d'erreurs dans des transcriptions OCR de documents historiques par r\'eseaux de neurones r\'ecurrents multi-niveau (Combining character level and word level RNNs for post-OCR error detection)—0
Detection Masking for Improved OCR on Noisy Documents—0
Reciprocal Feature Learning via Explicit and Implicit Tasks in Scene Text Recognition—0
Recognition of Images of Korean Characters Using Embedded Networks—0
Recognition of Text Image Using Multilayer Perceptron—0
Recommending Scientific Videos based on Metadata Enrichment using Linked Open Data—0
Reconnaissance d’entités nommées sur des sorties OCR bruitées : des pistes pour la désambiguïsation morphologique automatique (Resolution of entity linking issues on noisy OCR output : automatic disambiguation tracks)—0
Recursive Recurrent Nets with Attention Modeling for OCR in the Wild—0
Reference-Based Post-OCR Processing with LLM for Diacritic Languages—0
Refining Corpora from a Model Calibration Perspective for Chinese Spelling Correction—0
Refining Text-to-Image Generation: Towards Accurate Training-Free Glyph-Enhanced Image Generation—0
Regularization and Kernelization of the Maximin Correlation Approach—0
ReLayout: Towards Real-World Document Understanding via Layout-enhanced Pre-training—0
Representing Online Handwriting for Recognition in Large Vision-Language Models—0
Reranking with Linguistic and Semantic Features for Arabic Optical Character Recognition—0
Resilience of Large Language Models for Noisy Instructions—0
Resolving Referring Expressions in Images With Labeled Elements—0
Resolving Sentiment Discrepancy for Multimodal Sentiment Detection via Semantics Completion and Decomposition—0
Resource Constrained Structured Prediction—0
Resume Information Extraction via Post-OCR Text Processing—0
Rethinking Visual Prompting for Multimodal Large Language Models with External Knowledge—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