Essay
Version 1
Preserved in Portico This version is not peer-reviewed
Machine Learning Methods for Handwriting Recognition
Version 1
: Received: 16 December 2023 / Approved: 18 December 2023 / Online: 18 December 2023 (13:46:31 CET)
How to cite: Peng, Y. Machine Learning Methods for Handwriting Recognition. Preprints 2023, 2023121301. https://doi.org/10.20944/preprints202312.1301.v1 Peng, Y. Machine Learning Methods for Handwriting Recognition. Preprints 2023, 2023121301. https://doi.org/10.20944/preprints202312.1301.v1
Abstract
Machine learning is a fundamental aspect of artificial intelligence that involves the development of algorithms and models that allow computers to learn and make predictions or decisions without explicit programming. With the development of neural networks, back-propagation algorithms and deep learning, machine learning has made breakthroughs in the fields of image recognition, natural language processing and handwriting recognition using machine learning techniques. The advent of deep learning has revolutionized the field of handwriting recognition, using convolutional neural networks, recurrent neural networks, and sequence-to-sequence models to provide solutions that go beyond machine learning methods and significantly improve the accuracy and robustness of handwriting recognition systems. But challenges remain, including the need for large labelled datasets, computational resources and addressing potential biases. As research in deep learning techniques continues to drive handwriting recognition closer towards realisability, machine learning approaches remain at the forefront.
Keywords
Handwriting recognition; Machine learning (ML); Deep learning; Transfer learning strategy; Handwritten datasets
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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