SOTAVerified

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
Spatial Context-based Self-Supervised Learning for Handwritten Text Recognition—0
Synthetic data generation for Indic handwritten text recognition—0
The Challenges of HTR Model Training: Feedback from the Project Donner le gout de l'archive a l'ere numerique—0
The LAM Dataset: A Novel Benchmark for Line-Level Handwritten Text Recognition—0
Towards Robust Handwritten Text Recognition with On-the-fly User Participation—0
TRIDIS: A Comprehensive Medieval and Early Modern Corpus for HTR and NER—0
Unsupervised Adaptation for Synthetic-to-Real Handwritten Word Recognition—0
Urdu Handwritten Text Recognition Using ResNet18—0
Variational Connectionist Temporal Classification—0
Vital Records: Uncover the past from historical handwritten records—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
Learning to Align: Addressing Character Frequency Distribution Shifts in Handwritten Text RecognitionCode0
Uncovering the Handwritten Text in the Margins: End-to-end Handwritten Text Detection and RecognitionCode0
Judge a Book by its Cover: Investigating Multi-Modal LLMs for Multi-Page Handwritten Document TranscriptionCode0
HTR-JAND: Handwritten Text Recognition with Joint Attention Network and Knowledge DistillationCode0
Start, Follow, Read: End-to-End Full-Page Handwriting RecognitionCode0
HANA: A HAndwritten NAme Database for Offline Handwritten Text RecognitionCode0
Handwritten Stenography Recognition and the LION DatasetCode0
No Padding Please: Efficient Neural Handwriting RecognitionCode0
End-to-End Page-Level Assessment of Handwritten Text RecognitionCode0
Character-Based Handwritten Text Transcription with Attention NetworksCode0
End-to-end Handwritten Paragraph Text Recognition Using a Vertical Attention NetworkCode0
Faster DAN: Multi-target Queries with Document Positional Encoding for End-to-end Handwritten Document RecognitionCode0
Writer adaptation for offline text recognition: An exploration of neural network-based methodsCode0
Evaluation of HTR models without Ground Truth MaterialCode0
Handwritten Paleographic Greek Text Recognition: A Century-Based ApproachCode0
Automatic Transcription of Handwritten Old Occitan LanguageCode0
Evaluating Sequence-to-Sequence Models for Handwritten Text RecognitionCode0
Platypus: A Generalized Specialist Model for Reading Text in Various FormsCode0
SPAN: a Simple Predict & Align Network for Handwritten Paragraph RecognitionCode0
HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout AnalysisCode0
BN-HTRd: A Benchmark Dataset for Document Level Offline Bangla Handwritten Text Recognition (HTR) and Line SegmentationCode0
Enhance to Read Better: A Multi-Task Adversarial Network for Handwritten Document Image EnhancementCode0
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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
5Start, Follow, ReadCER6.4—Unverified
6Decouple Attention NetworkCER6.4—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
3DANTest CER4.1—Unverified
4VANTest 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