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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 1026–1050 of 1209 papers

TitleStatusHype
A Survey on Optical Character Recognition System—0
A Diachronic Corpus for Romanian (RoDia)—0
Multi-modular domain-tailored OCR post-correction—0
Improving Document Clustering by Removing Unnatural Language—0
Transliterated Mobile Keyboard Input via Weighted Finite-State Transducers—0
The Labeled Segmentation of Printed Books—0
Word Searching in Scene Image and Video Frame in Multi-Script Scenario using Dynamic Shape Coding—0
Sequence-to-Label Script Identification for Multilingual OCR—0
Convolutional Neural Networks for Font Classification—0
STN-OCR: A single Neural Network for Text Detection and Text RecognitionCode0
A Novel Transfer Learning Approach upon Hindi, Arabic, and Bangla Numerals using Convolutional Neural Networks—0
Text Recognition in Scene Image and Video Frame using Color Channel Selection—0
A second-order orientation-contrast stimulus for population-receptive-field-based retinotopic mapping—0
Arabic Character Segmentation Using Projection Based Approach with Profile's Amplitude Filter—0
Single Classifier-based Passive System for Source Printer Classification using Local Texture FeaturesCode0
SEARNN: Training RNNs with Global-Local LossesCode0
Text Extraction From Texture Images Using Masked Signal Decomposition—0
Traitement des Mots Hors Vocabulaire pour la Traduction Automatique de Document OCRis\'es en Arabe (This article presents a new system that automatically translates images of arabic documents)—0
Handwritten Urdu Character Recognition using 1-Dimensional BLSTM Classifier—0
Derivate-based Component-Trees for Multi-Channel Image Segmentation—0
The Making of the Royal Society Corpus—0
Tagging Named Entities in 19th Century and Modern Finnish Newspaper Material with a Finnish Semantic Tagger—0
OCR and post-correction of historical Finnish texts—0
Improving Optical Character Recognition of Finnish Historical Newspapers with a Combination of Fraktur \& Antiqua Models and Image Preprocessing—0
Applying BLAST to Text Reuse Detection in Finnish Newspapers and Journals, 1771-1910—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