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 476–500 of 1209 papers

TitleStatusHype
From Machine Learning to Machine Reasoning—0
An End-to-End Khmer Optical Character Recognition using Sequence-to-Sequence with Attention—0
An Ensemble of Neural Networks for Non-Linear Segmentation of Overlapped Cursive Script—0
Detection Masking for Improved OCR on Noisy Documents—0
Chunk-based Chinese Spelling Check with Global Optimization—0
Bangla Natural Language Processing: A Comprehensive Analysis of Classical, Machine Learning, and Deep Learning Based Methods—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
Bambara and Maninka Manding Languages Written Corpora Project (``Projet des corpus \'ecrits des langues manding : le bambara, le maninka'') [in French]—0
Detecting de minimis Code-Switching in Historical German Books—0
Classification of Documents Extracted from Images with Optical Character Recognition Methods—0
Design and Implementation of an OCR-Powered Pipeline for Table Extraction from Invoices—0
Generalization or Hallucination? Understanding Out-of-Context Reasoning in Transformers—0
Generalizing Analytic Shrinkage for Arbitrary Covariance Structures—0
Balanced Korean Word Spacing with Structural SVM—0
Post-OCR Paragraph Recognition by Graph Convolutional Networks—0
Generating a Training Corpus for OCR Post-Correction Using Encoder-Decoder Model—0
Generating Monolingual Dataset for Low Resource Language Bodo from old books using Google Keep—0
Design and Development of a Framework For Stroke-Based Handwritten Gujarati Font Generation—0
Derivate-based Component-Trees for Multi-Channel Image Segmentation—0
Advancing Vehicle Plate Recognition: Multitasking Visual Language Models with VehiclePaliGemma—0
Hierarchical Visual Feature Aggregation for OCR-Free Document Understanding—0
Geometric Rectification of Creased Document Images based on Isometric Mapping—0
Delta vs. N-Gram Tracing: Evaluating the Robustness of Authorship Attribution Methods—0
Harnessing PDF Data for Improving Japanese Large Multimodal Models—0
Auto-ML Deep Learning for Rashi Scripts OCR—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