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Handwritten Text Recognition
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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.
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ScrabbleGAN: Semi-Supervised Varying Length Handwritten Text Generation
Decoupled Attention Network for Text Recognition
A limited-size ensemble of homogeneous CNN/LSTMs for high-performance word classification
A Computationally Efficient Pipeline Approach to Full Page Offline Handwritten Text Recognition
Unsupervised Adaptation for Synthetic-to-Real Handwritten Word Recognition
Fully Convolutional Networks for Handwriting Recognition
BADAM: A Public Dataset for Baseline Detection in Arabic-script Manuscripts
End to End Recognition System for Recognizing Offline Unconstrained Vietnamese Handwriting
A Scalable Handwritten Text Recognition System
Early warning in egg production curves from commercial hens: A SVM approach
Evaluating Sequence-to-Sequence Models for Handwritten Text Recognition
Manifold Mixup improves text recognition with CTC loss
No Padding Please: Efficient Neural Handwriting Recognition
Are 2D-LSTM really dead for offline text recognition?
Learning to Read by Spelling: Towards Unsupervised Text Recognition
An Efficient End-to-End Neural Model for Handwritten Text Recognition
Bench-Marking Information Extraction in Semi-Structured Historical Handwritten Records
Synthetic data generation for Indic handwritten text recognition
Character-Based Handwritten Text Transcription with Attention Networks
Regular expressions for decoding of neural network outputs
Handwritten Text Recognition Results on the Bentham Collection with Improved Classical N-Gram-HMM methods
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