Bounded Box-Zoning Integrated Approach For Children Handwriting Recognition - Qausbee, Nik Nur Adlin Nik (2024)
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.