Article

Personalized handwriting recognition via biased regularization

Authors:

Wolf Kienzle,

Kumar ChellapillaAuthors Info & Claims

ICML '06: Proceedings of the 23rd international conference on Machine learning

Pages 457 - 464

https://doi.org/10.1145/1143844.1143902

Published: 25 June 2006 Publication History

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Abstract

We present a new approach to personalized handwriting recognition. The problem, also known as writer adaptation, consists of converting a generic (user-independent) recognizer into a personalized (user-dependent) one, which has an improved recognition rate for a particular user. The adaptation step usually involves user-specific samples, which leads to the fundamental question of how to fuse this new information with that captured by the generic recognizer. We propose adapting the recognizer by minimizing a regularized risk functional (a modified SVM) where the prior knowledge from the generic recognizer enters through a modified regularization term. The result is a simple personalization framework with very good practical properties. Experiments on a 100 class real-world data set show that the number of errors can be reduced by over 40% with as few as five user samples per character.

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Du JLiu PVong CChen CWang TChen C(2024)Class-Incremental Learning Method With Fast Update and High Retainability Based on Broad Learning SystemIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.325901635:8(11332-11345)Online publication date: Aug-2024
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Choi JLee JLee CChang J(2023)M-CTRL: A Continual Representation Learning Framework with Slowly Improving Past Pre-Trained ModelICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP49357.2023.10096793(1-5)Online publication date: 4-Jun-2023
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Index Terms

Personalized handwriting recognition via biased regularization
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
  2. Machine learning

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Information

Published In

ICML '06: Proceedings of the 23rd international conference on Machine learning

June 2006

1154 pages

ISBN:1595933832

DOI:10.1145/1143844

Program Chairs:
William Cohen,
Andrew Moore

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 June 2006

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ICML '06 Paper Acceptance Rate 140 of 548 submissions, 26%;

Overall Acceptance Rate 140 of 548 submissions, 26%

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Du JLiu PVong CChen CWang TChen C(2024)Class-Incremental Learning Method With Fast Update and High Retainability Based on Broad Learning SystemIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.325901635:8(11332-11345)Online publication date: Aug-2024
https://doi.org/10.1109/TNNLS.2023.3259016
Choi JLee JLee CChang J(2023)M-CTRL: A Continual Representation Learning Framework with Slowly Improving Past Pre-Trained ModelICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP49357.2023.10096793(1-5)Online publication date: 4-Jun-2023
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Jose SSimeone O(2021)Information-Theoretic Generalization Bounds for Meta-Learning and ApplicationsEntropy10.3390/e2301012623:1(126)Online publication date: 19-Jan-2021
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