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 876–900 of 1209 papers

TitleStatusHype
Real-time Scene Text Detection with Differentiable BinarizationCode2
Handwritten and Machine printed OCR for Geez Numbers Using Artificial Neural Network—0
Character Keypoint-based Homography Estimation in Scanned Documents for Efficient Information Extraction—0
Vehicle-Rear: A New Dataset to Explore Feature Fusion for Vehicle Identification Using Convolutional Neural NetworksCode0
Recognition of Images of Korean Characters Using Embedded Networks—0
Improving Long Handwritten Text Line Recognition with Convolutional Multi-way Associative Memory—0
VASTA: A Vision and Language-assisted Smartphone Task Automation System—0
BIOfid Dataset: Publishing a German Gold Standard for Named Entity Recognition in Historical Biodiversity Literature—0
DeepErase: Weakly Supervised Ink Artifact Removal in Document Text ImagesCode0
From the Paft to the Fiiture: a Fully Automatic NMT and Word Embeddings Method for OCR Post-CorrectionCode0
Rosetta: Large scale system for text detection and recognition in images—0
MIDV-2019: Challenges of the modern mobile-based document OCRCode0
Membership Model Inversion Attacks for Deep Networks—0
Parallel Iterative Edit Models for Local Sequence TransductionCode0
NASS-AI: Towards Digitization of Parliamentary Bills using Document Level Embedding and Bidirectional Long Short-Term MemoryCode0
DewarpNet: Single-Image Document Unwarping With Stacked 3D and 2D Regression NetworksCode0
Multi-modal Page Stream Segmentation with Convolutional Neural NetworksCode0
Document Rectification and Illumination Correction using a Patch-based CNNCode0
EATEN: Entity-aware Attention for Single Shot Visual Text ExtractionCode0
Corporate IT-support Help-Desk Process Hybrid-Automation Solution with Machine Learning Approach—0
Fast Search with Poor OCR—0
Post-OCR parsing: building simple and robust parser via BIO taggingCode0
Chargrid-OCR: End-to-end trainable Optical Character Recognition through Semantic Segmentation and Object Detection—0
CORD: A Consolidated Receipt Dataset for Post-OCR ParsingCode0
SVDocNet: Spatially Variant U-Net for Blind Document Deblurring—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