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 301–325 of 1209 papers

TitleStatusHype
A Novel Transfer Learning Approach upon Hindi, Arabic, and Bangla Numerals using Convolutional Neural Networks—0
DocXChain: A Powerful Open-Source Toolchain for Document Parsing and Beyond—0
Building OCR/NER Test Collections—0
A Novel Pipeline for Improving Optical Character Recognition through Post-processing Using Natural Language Processing—0
A Novel Method for the Recognition of Isolated Handwritten Arabic Characters—0
Building A Handwritten Cuneiform Character Imageset—0
Building a Corpus from Handwritten Picture Postcards: Transcription, Annotation and Part-of-Speech Tagging—0
Document Layout Analysis via Dynamic Residual Feature Fusion—0
SAR-Net: Shape Alignment and Recovery Network for Category-level 6D Object Pose and Size Estimation—0
Budget-Optimal Task Allocation for Reliable Crowdsourcing Systems—0
BROS: A Pre-trained Language Model for Understanding Texts in Document—0
A Novel Machine Learning Based Approach for Post-OCR Error Detection—0
Broken News: Making Newspapers Accessible to Print-Impaired—0
A Novel Approach to Skew-Detection and Correction of English Alphabets for OCR—0
A Holistic Approach for Optimizing DSP Block Utilization of a CNN implementation on FPGA—0
A Novel Approach to OCR using Image Recognition based Classification for Ancient Tamil Inscriptions in Temples—0
Braille-to-Speech Generator: Audio Generation Based on Joint Fine-Tuning of CLIP and Fastspeech2—0
A Conglomerate of Multiple OCR Table Detection and Extraction—0
BoundingDocs: a Unified Dataset for Document Question Answering with Spatial Annotations—0
Bootstrapping a historical commodities lexicon with SKOS and DBpedia—0
3D Rendering Framework for Data Augmentation in Optical Character Recognition—0
Document Image Binarization in JPEG Compressed Domain using Dual Discriminator Generative Adversarial Networks—0
Dynamic Programming Approach to Template-based OCR—0
Boosting Optical Character Recognition: A Super-Resolution Approach—0
Boosting High-Level Vision with Joint Compression Artifacts Reduction and Super-Resolution—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