Empowering the Visually Impaired: Translating Handwritten Digits into Spoken Language with HRNN-GOA and Haralick Features

Abstract

Visual impairment poses significant challenges to individuals in their daily lives, limiting their access to information encoded in the visual domain. This paper presents a novel approach to empower the visually impaired by developing a system capable of translating handwritten digits into spoken language. The proposed system leverages a combination of advanced deep learning (DL) architecture, Hopfield Recurrent Neural Network-Grasshopper Optimization Algorithm (HRNN-GOA), and traditional image-processing techniques such as Haralick features. The system employs HRNN-GOA as the core model for handwritten digit recognition. HRNN-GOA exhibits superior sequential learning capabilities, capturing intricate patterns in the handwritten digits. Additionally, Haralick features are extracted from the input images, providing complementary texture-based information. The fusion of DL and traditional features aims to enhance the robustness and accuracy of the recognition process. The experimental results demonstrate the effectiveness of the proposed approach in accurately recognising handwritten digits. The HRNN-GOA model achieves state-of-the-art performance in digit classification tasks, while the incorporation of Haralick features further refines the recognition process, especially in cases with complex textures or variations in writing styles. The simulation results are compared against state-of-the-art strategies in terms of many metrics, including accuracy, precision, recall, specificity, area under the curve, F1-score, and false-positive rate. The proposed system has the potential to significantly improve the independence and quality of life for individuals with visual impairments by providing seamless access to numerical information in a spoken format. Future endeavours could explore the extension of this framework to recognise and translate more complex handwritten symbols or characters. Additionally, user experience studies and real-world deployment assessments will be crucial for refining the system and ensuring its practical utility in diverse scenarios.