Handwriting Recognition
50 papers with code • 3 benchmarks • 20 datasets
Benchmarks
These leaderboards are used to track progress in Handwriting Recognition
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Libraries
Use these libraries to find Handwriting Recognition models and implementations
IAM
DeepWriting
RIMES
HKR
Bentham
Ricordi
Konzil
Schiller
Latest papers with no code
CENSUS-HWR: a large training dataset for offline handwriting recognition
Progress in Automated Handwriting Recognition has been hampered by the lack of large training datasets.
Online Gesture Recognition using Transformer and Natural Language Processing
The Transformer architecture is shown to provide a powerful machine transduction framework for online handwritten gestures corresponding to glyph strokes of natural language sentences.
How to Choose Pretrained Handwriting Recognition Models for Single Writer Fine-Tuning
Recent advancements in Deep Learning-based Handwritten Text Recognition (HTR) have led to models with remarkable performance on both modern and historical manuscripts in large benchmark datasets.
Key-value information extraction from full handwritten pages
We propose a Transformer-based approach for information extraction from digitized handwritten documents.
SIMARA: a database for key-value information extraction from full pages
We propose a new database for information extraction from historical handwritten documents.
MSdocTr-Lite: A Lite Transformer for Full Page Multi-script Handwriting Recognition
In this paper, we propose a lite transformer architecture for full-page multi-script handwriting recognition.
Towards Writing Style Adaptation in Handwriting Recognition
We experimented with various placements and settings of WSB and contrastively pre-trained embeddings.
Finetuning Is a Surprisingly Effective Domain Adaptation Baseline in Handwriting Recognition
In many machine learning tasks, a large general dataset and a small specialized dataset are available.
Representation Learning for Tablet and Paper Domain Adaptation in Favor of Online Handwriting Recognition
The goal of domain adaptation (DA) is to mitigate this domain shift problem by searching for an optimal feature transformation to learn a domain-invariant representation.
The Challenges of HTR Model Training: Feedback from the Project Donner le gout de l'archive a l'ere numerique
The arrival of handwriting recognition technologies offers new possibilities for research in heritage studies.
