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
KOHTD
Schiller
Ricordi
Most implemented papers
Improving Accuracy and Explainability of Online Handwriting Recognition
A wide range of applications from signature verification to electronic document processing can be realized by implementing efficient and accurate handwriting recognition algorithms.
Towards End-to-end Handwritten Document Recognition
We proposed an approach at the line level, based on a fully convolutional network, in order to design a first generic feature extraction step for the handwriting recognition task.
Kurdish Handwritten Character Recognition using Deep Learning Techniques
RebinMA/KHCR
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From the experimental results, it is clear that the proposed deep learning model is performing well and is comparable to the similar model of other languages' handwriting recognition systems.
UIT-HWDB: Using Transferring Method to Construct A Novel Benchmark for Evaluating Unconstrained Handwriting Image Recognition in Vietnamese
Recognizing handwriting images is challenging due to the vast variation in writing style across many people and distinct linguistic aspects of writing languages.
SoftCTC -- Semi-Supervised Learning for Text Recognition using Soft Pseudo-Labels
dcgm/softctc
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This paper explores semi-supervised training for sequence tasks, such as Optical Character Recognition or Automatic Speech Recognition.
Huruf: An Application for Arabic Handwritten Character Recognition Using Deep Learning
minhazkamal/arabic-handwritten-character-and-digit-recognition
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Prominent work has been done in this field focusing mainly on Latin characters.
BN-DRISHTI: Bangla Document Recognition through Instance-level Segmentation of Handwritten Text Images
Handwriting recognition remains challenging for some of the most spoken languages, like Bangla, due to the complexity of line and word segmentation brought by the curvilinear nature of writing and lack of quality datasets.
FPGA Implementation of Convolutional Neural Network for Real-Time Handwriting Recognition
Machine Learning (ML) has recently been a skyrocketing field in Computer Science.
Writer adaptation for offline text recognition: An exploration of neural network-based methods
tobiasvanderwerff/master-thesis
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In this paper, we explore how HTR models can be made writer adaptive by using only a handful of examples from a new writer (e. g., 16 examples) for adaptation.
Character Queries: A Transformer-based Approach to On-Line Handwritten Character Segmentation
On-line handwritten character segmentation is often associated with handwriting recognition and even though recognition models include mechanisms to locate relevant positions during the recognition process, it is typically insufficient to produce a precise segmentation.
