Linear discriminant analysis-based deep learning algorithms for numerical character handwriting recognition
Abstract
Information processing requires handwritten digit recognition, but methods of writing and image defects like brightness changes, blurring, and noise make image recognition challenging. This paper presents a strategy for categorizing offline handwritten digits in both Devanagari script and Roman script (English numbers) using Deep Learning (DL) algorithms, a branch of Machine Learning (ML) that uses Neural Networks (NN) with multiple layers to acquire hierarchical representations of input autonomously. The research study develops classification algorithms for recognising handwritten digits in numerical characters (0–9), analyzing classifier combination approaches, and determining their accuracy. The study aims to optimize recognition results when working with multiple scripts simultaneously. It proposes a simple profiling technique, Linear Discriminant Analysis (LDA) implementation, and a NN structure for numerical character classification. However, testing shows inconsistent outcomes from the LDA classifier. The approach, which combines profile-based Feature Extraction (FE) with advanced classification algorithms, can significantly improve the field of HWR numerical characters, as evidenced by the diverse outcomes it produces. The model performed 98.98% on the MNIST dataset. In the CPAR database, we completed a cross-dataset evaluation with 98.19% accuracy.
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DOI: https://doi.org/10.32629/jai.v7i5.1621
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