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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
Handwritten Digit Recognition using Machine and Deep Learning Algorithms—0
Enhance to Read Better: A Multi-Task Adversarial Network for Handwritten Document Image EnhancementCode0
One-shot Compositional Data Generation for Low Resource Handwritten Text Recognition—0
Rethinking Text Line Recognition Models—0
Urdu Handwritten Text Recognition Using ResNet18—0
SPAN: a Simple Predict & Align Network for Handwritten Paragraph RecognitionCode0
Applications of Machine Learning in Document Digitisation—0
HANA: A HAndwritten NAme Database for Offline Handwritten Text RecognitionCode0
Enhancing Handwritten Text Recognition with N-gram sequence decomposition and Multitask Learning—0
Have convolutions already made recurrence obsolete for unconstrained handwritten text recognition ?—0
End-to-end Handwritten Paragraph Text Recognition Using a Vertical Attention NetworkCode0
Boosting offline handwritten text recognition in historical documents with few labeled lines—0
Vital Records: Uncover the past from historical handwritten records—0
EASTER: Efficient and Scalable Text Recognizer—0
Variational Connectionist Temporal Classification—0
Pay Attention to What You Read: Non-recurrent Handwritten Text-Line Recognition—0
Books of Hours. the First Liturgical Data Set for Text Segmentation.—0
How Much Data Do You Need? About the Creation of a Ground Truth for Black Letter and the Effectiveness of Neural OCR—0
A limited-size ensemble of homogeneous CNN/LSTMs for high-performance word classification—0
A Computationally Efficient Pipeline Approach to Full Page Offline Handwritten Text Recognition—0
Unsupervised Adaptation for Synthetic-to-Real Handwritten Word Recognition—0
On recognition of Cyrillic Text—0
Fully Convolutional Networks for Handwriting Recognition—0
BADAM: A Public Dataset for Baseline Detection in Arabic-script Manuscripts—0
An Alternative Deep Feature Approach to Line Level Keyword Spotting—0
End to End Recognition System for Recognizing Offline Unconstrained Vietnamese Handwriting—0
A Scalable Handwritten Text Recognition System—0
Early warning in egg production curves from commercial hens: A SVM approach—0
Evaluating Sequence-to-Sequence Models for Handwritten Text RecognitionCode0
No Padding Please: Efficient Neural Handwriting RecognitionCode0
Are 2D-LSTM really dead for offline text recognition?—0
Start, Follow, Read: End-to-End Full-Page Handwriting RecognitionCode0
An Efficient End-to-End Neural Model for Handwritten Text Recognition—0
Bench-Marking Information Extraction in Semi-Structured Historical Handwritten Records—0
Synthetic data generation for Indic handwritten text recognition—0
Character-Based Handwritten Text Transcription with Attention NetworksCode0
Regular expressions for decoding of neural network outputs—0
An LDA-based Topic Selection Approach to Language Model Adaptation for Handwritten Text Recognition—0
Handwritten Text Recognition Results on the Bentham Collection with Improved Classical N-Gram-HMM methods—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