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Handwritten Text Recognition

Handwritten Text Recognition (HTR) is the task of automatically identifying and transcribing handwritten text from images or scanned documents into machine-readable text. The goal is to develop a system capable of accurately interpreting diverse handwriting styles, accounting for variations in alignment, stroke, spacing, and noise. This task involves detecting handwritten regions within an image, extracting the text content, and converting it into a structured digital format, enabling further search, indexing, or data analysis.

Papers

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Showing 101–139 of 139 papers

TitleStatusHype
Benchmarking Large Language Models for Handwritten Text Recognition—0
Books of Hours. the First Liturgical Data Set for Text Segmentation.—0
Boosting Modern and Historical Handwritten Text Recognition with Deformable Convolutions—0
Boosting offline handwritten text recognition in historical documents with few labeled lines—0
Col-OLHTR: A Novel Framework for Multimodal Online Handwritten Text Recognition—0
Content and Style Aware Generation of Text-line Images for Handwriting Recognition—0
CSSL-MHTR: Continual Self-Supervised Learning for Scalable Multi-script Handwritten Text Recognition—0
DARE: A large-scale handwritten date recognition system—0
Early warning in egg production curves from commercial hens: A SVM approach—0
EASTER: Efficient and Scalable Text Recognizer—0
End-to-end information extraction in handwritten documents: Understanding Paris marriage records from 1880 to 1940—0
End to End Recognition System for Recognizing Offline Unconstrained Vietnamese Handwriting—0
Enhancing Handwritten Text Recognition with N-gram sequence decomposition and Multitask Learning—0
Fully Convolutional Networks for Handwriting Recognition—0
GatedLexiconNet: A Comprehensive End-to-End Handwritten Paragraph Text Recognition System—0
Handling Heavily Abbreviated Manuscripts: HTR engines vs text normalisation approaches—0
Handwritten Digit Recognition using Machine and Deep Learning Algorithms—0
Handwritten Text Recognition: A Survey—0
Handwritten Text Recognition from Crowdsourced Annotations—0
Handwritten Text Recognition (HTR) for Irish-Language Folklore—0
Handwritten Text Recognition Results on the Bentham Collection with Improved Classical N-Gram-HMM methods—0
Handwritten Text Recognition Using Convolutional Neural Network—0
HATFormer: Historic Handwritten Arabic Text Recognition with Transformers—0
Have convolutions already made recurrence obsolete for unconstrained handwritten text recognition ?—0
How Much Data Do You Need? About the Creation of a Ground Truth for Black Letter and the Effectiveness of Neural OCR—0
How to Choose Pretrained Handwriting Recognition Models for Single Writer Fine-Tuning—0
Improving Amharic Handwritten Word Recognition Using Auxiliary Task—0
Integrating Canonical Neural Units and Multi-Scale Training for Handwritten Text Recognition—0
Large Scale Genealogical Information Extraction From Handwritten Quebec Parish Records—0
Lexicon and Attention based Handwritten Text Recognition System—0
A Comprehensive Handwritten Paragraph Text Recognition System: LexiconNet—0
MetaWriter: Personalized Handwritten Text Recognition Using Meta-Learned Prompt Tuning—0
MSdocTr-Lite: A Lite Transformer for Full Page Multi-script Handwriting Recognition—0
Nunc profana tractemus. Detecting Code-Switching in a Large Corpus of 16th Century Letters—0
Nuremberg Letterbooks: A Multi-Transcriptional Dataset of Early 15th Century Manuscripts for Document Analysis—0
One-shot Compositional Data Generation for Low Resource Handwritten Text Recognition—0
On recognition of Cyrillic Text—0
On the Generalization of Handwritten Text Recognition Models—0
Open Source Handwritten Text Recognition on Medieval Manuscripts using Mixed Models and Document-Specific Finetuning—0
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All datasetsIAMLAM(line-level)IAM(line-level)READ2016(line-level)BelfortREAD 2016BenthamDigital PeterHKRIAM-BIAM-DSaint Gall

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Transformer w/ CNNCER7.62—Unverified
2FPHR Paragraph Level (~145 dpi)CER6.7—Unverified
3Leaky LP CellCER6.6—Unverified
4FPHR+Aug Line Level (~145 dpi)CER6.5—Unverified
5Decouple Attention NetworkCER6.4—Unverified
6Start, Follow, ReadCER6.4—Unverified
7FPHR+Aug Paragraph Level (~145 dpi)CER6.3—Unverified
8Easter2.0CER6.21—Unverified
9HTR-VT(line-level)CER4.7—Unverified
10Transformer w/ CNN (+synth)CER4.67—Unverified
#ModelMetricClaimedVerifiedStatus
1GFCNTest CER5.2—Unverified
2TrOCRTest CER3.6—Unverified
3OrigamiNet-18Test CER3.1—Unverified
4OrigamiNet-12Test CER3.1—Unverified
5OrigamiNet-24Test CER3—Unverified
6HTR-VTTest CER2.8—Unverified
#ModelMetricClaimedVerifiedStatus
1GFCNTest CER8—Unverified
2OrigamiNet-12Test CER6—Unverified
3VANTest CER5—Unverified
4HTR-VTTest CER4.7—Unverified
5TrOCRTest CER3.4—Unverified
#ModelMetricClaimedVerifiedStatus
1CNN + BLSTMTest CER4.7—Unverified
2SpanTest CER4.6—Unverified
3VANTest CER4.1—Unverified
4DANTest CER4.1—Unverified
5HTR-VTTest CER3.9—Unverified
#ModelMetricClaimedVerifiedStatus
1PyLaia (human transcriptions + random split)CER (%)10.54—Unverified
2PyLaia (human transcriptions + agreement-based split)CER (%)5.57—Unverified
3PyLaia (rover consensus + agreement-based split)CER (%)4.95—Unverified
4PyLaia (all transcriptions + agreement-based split)CER (%)4.34—Unverified
#ModelMetricClaimedVerifiedStatus
1HTR-VT(line-level)CER (%)3.9—Unverified
2DANCER (%)3.22—Unverified
#ModelMetricClaimedVerifiedStatus
1StackMix+BlotsCER1.73—Unverified
#ModelMetricClaimedVerifiedStatus
1StackMix+BlotsCER2.5—Unverified
#ModelMetricClaimedVerifiedStatus
1StackMix+BlotsCER3.49—Unverified
#ModelMetricClaimedVerifiedStatus
1StackMix+BlotsCER3.77—Unverified
#ModelMetricClaimedVerifiedStatus
1StackMix+BlotsCER3.01—Unverified
#ModelMetricClaimedVerifiedStatus
1StackMix+BlotsCER3.65—Unverified
#ModelMetricClaimedVerifiedStatus
1DANCER (%)6.46—Unverified