Enhancing handwritten numeric string recognition through incremental support vector machines

Rani Kurnia Putri; Muhammad Athoillah

doi:10.59400/jam.v2i1.373

Enhancing handwritten numeric string recognition through incremental support vector machines

Rani Kurnia Putri Department of Mathematics Education, Universitas PGRI Adi Buana Surabaya, Jawa Timur 60234, Indonesia
Muhammad Athoillah Department of Statistics, Universitas PGRI Adi Buana Surabaya, Jawa Timur 60234, Indonesia http://orcid.org/0000-0002-1800-1314

Article ID: 373

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DOI: https://doi.org/10.59400/jam.v2i1.373

Keywords: digital image processing; incremental learning; pattern recognition; dynamic machine learning

Abstract

Handwritten digit recognition systems are integral to diverse applications such as postal services, banking, and document processing in our digitally-driven society. This research addresses the challenges posed by evolving datasets and dynamic scenarios in handwritten digit recognition by proposing an approach based on incremental support vector machines (ISVM). ISVM is an extension of traditional support vector machines (SVM) designed to handle scenarios where new data points become available over time. The dataset includes handwritten images (numbers “0” to “6”) and trials introducing new classes (“7”, “8”, and “9”). Evaluation utilizes k-fold cross-validation for robustness. Digital image processing involves converting images into numeric data using the histogram method. The result showed the positive outcomes of using ISVM in handwritten digit recognition, emphasizing its adaptability to incremental learning and its ability to maintain robust performance in the face of evolving datasets, which is crucial for real-world applications.

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Published

2024-01-19

How to Cite

Putri, R. K., & Athoillah, M. (2024). Enhancing handwritten numeric string recognition through incremental support vector machines. Journal of AppliedMath, 2(1), 373. https://doi.org/10.59400/jam.v2i1.373

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