Cash reader for the visually impaired individuals

Tan, Nian Herng (2024) Cash reader for the visually impaired individuals. Final Year Project, UTAR.

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Abstract

Visually impaired individuals continuously face countless challenges in their daily lives. Cash Reader stands at the forefront of accurate detection and recognition for banknotes and coins that could be used by visually impaired individuals. Traditional methods that are used to address the challenges of visually impaired individuals in recognizing the value of currency, such as the use of tactile features or Braille labels on banknotes, are helpful but come with significant limitations. Tactile features and Braille labels are not universally available, and even when present, they may not be effective for all users, particularly those with diminished tactile sensitivity or those who do not read Braille. Banknote size and shape differences, while useful, can lead to confusion, especially when new designs are introduced or when notes are worn. Folding techniques, although practical, can be cumbersome and prone to errors, while banknote templates are often inconvenient to carry and use. Additionally, reliance on assistance from others compromises the independence of visually impaired individuals and increases the risk of errors or fraud. To overcome these challenges, the Cash Reader harnesses advanced deep learning techniques and AI to offer a robust and intuitive solution for currency recognition. Leveraging powerful models such as OpenCV and YOLOv8, it provides an accessible and highly accurate method for visually impaired individuals to independently identify Malaysian Ringgit, Malaysian coins, USD banknotes, EURO banknotes, SGD banknotes, Thai Baht banknotes and coins. The system is engineered to accurately distinguish between these denominations, enabling users to manage financial transactions with ease. By integrating these technologies, the Cash Reader promotes greater autonomy and confidence in daily financial interactions, reducing dependence on others and minimizing errors.

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