Bounded Box-Zoning Integrated Approach For Children Handwriting Recognition

Qausbee, Nik Nur Adlin Nik (2024) Bounded Box-Zoning Integrated Approach For Children Handwriting Recognition. PhD thesis, Perpustakaan Hamzah Sendut.

[img]
Preview
PDF
Download (297kB) | Preview

Abstract

This study focuses on recognizing handwritten character datasets of children through the integration of image processing and machine learning. The bounding box, one of the best structural feature extraction methods, has demonstrated high performance in the optical character recognition (OCR) pipeline. However, its implementation in children’s handwriting has shown a decreasing trend in alphabet detection. Similarly, zoning, a powerful technique under statistical feature extraction demonstrated good classification but is limited by having unlimited feature values and is not applicable for characters with high variations, such as children’s handwriting. The objectives of this study are to identify significant English alphabets based on their features, propose a bounded box-zoning integrated approach to improve the OCR pipeline and identify the accuracy of the proposed method. The Minnesota Handwriting Assessment (MHA) was utilized for data collection, involving handwriting samples collected from 90 children aged between 6 to 9 years old. The study then proceeded with image processing steps, including alphabet grouping into ‘small’ and ‘tail’ groups, feature extraction using the proposed hybrid method (bounded box-zoning), and classification using the multi-input neural network method.

Item Type: Thesis (PhD)
Subjects: Q Science > QA Mathematics > QA75.5-76.95 Electronic computers. Computer science
Divisions: Pusat Pengajian Sains Komputer (School of Computer Sciences) > Thesis
Depositing User: Mr Hasmizar Mansor
Date Deposited: 23 May 2025 02:05
Last Modified: 23 May 2025 02:05
URI: http://eprints.usm.my/id/eprint/62282

Actions (login required)

View Item View Item
Share