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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 451–475 of 1209 papers

TitleStatusHype
Extraction of Line Word Character Segments Directly from Run Length Compressed Printed Text Documents—0
ExTTNet: A Deep Learning Algorithm for Extracting Table Texts from Invoice Images—0
Eyes Closed, Safety On: Protecting Multimodal LLMs via Image-to-Text Transformation—0
ChartEye: A Deep Learning Framework for Chart Information Extraction—0
Fast Implementation of 4-bit Convolutional Neural Networks for Mobile Devices—0
Fast Search with Poor OCR—0
ChartParser: Automatic Chart Parsing for Print-Impaired—0
Development of a New Image-to-text Conversion System for Pashto, Farsi and Traditional Chinese—0
Fetch-A-Set: A Large-Scale OCR-Free Benchmark for Historical Document Retrieval—0
Detection of Text Reuse in French Medical Corpora—0
Bangla Text Recognition from Video Sequence: A New Focus—0
Finding Names in Trove: Named Entity Recognition for Australian Historical Newspapers—0
An End-to-End Khmer Optical Character Recognition using Sequence-to-Sequence with Attention—0
Finite State Approach to the Kazakh Nominal Paradigm—0
Chaurah: A Smart Raspberry Pi based Parking System—0
FLELex: a graded lexical resource for French foreign learners—0
An Ensemble of Neural Networks for Non-Linear Segmentation of Overlapped Cursive Script—0
GUI Action Narrator: Where and When Did That Action Take Place?—0
Detection Masking for Improved OCR on Noisy Documents—0
Bangla Natural Language Processing: A Comprehensive Analysis of Classical, Machine Learning, and Deep Learning Based Methods—0
Font Identification in Historical Documents Using Active Learning—0
Fooling OCR Systems with Adversarial Text Images—0
FormGym: Doing Paperwork with Agents—0
Fraunhofer SIT at CheckThat! 2023: Mixing Single-Modal Classifiers to Estimate the Check-Worthiness of Multi-Modal Tweets—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
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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
1DTrOCR 105MAccuracy (%)89.6—Unverified
2DTrOCRAccuracy (%)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