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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 151–175 of 1209 papers

TitleStatusHype
Document Dewarping with Control PointsCode1
XYLayoutLM: Towards Layout-Aware Multimodal Networks For Visually-Rich Document UnderstandingCode1
DiT: Self-supervised Pre-training for Document Image TransformerCode1
TableFormer: Table Structure Understanding with TransformersCode1
OCR-IDL: OCR Annotations for Industry Document Library DatasetCode1
On the Cross-dataset Generalization in License Plate RecognitionCode1
LaTr: Layout-Aware Transformer for Scene-Text VQACode1
An Automatic Approach for Generating Rich, Linked Geo-Metadata from Historical Map ImagesCode1
Indian Licence Plate Dataset in the wildCode1
Lexically Aware Semi-Supervised Learning for OCR Post-CorrectionCode1
DocScanner: Robust Document Image Rectification with Progressive LearningCode1
DocTr: Document Image Transformer for Geometric Unwarping and Illumination CorrectionCode1
WenetSpeech: A 10000+ Hours Multi-domain Mandarin Corpus for Speech RecognitionCode1
Rerunning OCR: A Machine Learning Approach to Quality Assessment and Enhancement PredictionCode1
TrOCR: Transformer-based Optical Character Recognition with Pre-trained ModelsCode1
Post-OCR Document Correction with large Ensembles of Character Sequence-to-Sequence ModelsCode1
BROS: A Pre-trained Language Model Focusing on Text and Layout for Better Key Information Extraction from DocumentsCode1
Lights, Camera, Action! A Framework to Improve NLP Accuracy over OCR documentsCode1
Robust Learning for Text Classification with Multi-source Noise Simulation and Hard Example MiningCode1
Implicit Feature Alignment: Learn to Convert Text Recognizer to Text SpotterCode1
End-to-End Information Extraction by Character-Level Embedding and Multi-Stage Attentional U-NetCode1
Multi-Type-TD-TSR -- Extracting Tables from Document Images using a Multi-stage Pipeline for Table Detection and Table Structure Recognition: from OCR to Structured Table RepresentationsCode1
Unknown-box Approximation to Improve Optical Character Recognition PerformanceCode1
AT-ST: Self-Training Adaptation Strategy for OCR in Domains with Limited TranscriptionsCode1
Operationalizing a National Digital Library: The Case for a Norwegian Transformer ModelCode1
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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