An Automated Ethiopian Banknote Recognition Model for Aiding Visual Impaired People

Even though there is an increasing demand on the use of Master cards and other electronic payment, money continues to be broadly utilized for ordinary exchanges due to its convenience. Nevertheless, the visually impaired people may suffer from recognizing each paper money. This necessitates the improved authenticity verification system that may assist visually disabled or blind people to identify denomination and realize if a paper money is authentic. This paper presents the development of a novel camera-based system that utilizes image processing technique with the aim of assisting visually disabled people to automatically recognize banknotes. The exclusive feature for each denomination specific ROI was extracted, and the recognition models were developed and tested. The proposed method first computes the dominant color of the banknotes. Then, the denomination specific ROI was identified automatically. In the end, the ColourMomentum, dominant color, and GLCM feature were calculated from each ROI. Lastly, the genetic optimization algorithm was applied for reducing the dimension of the feature vector. The proposed denomination specific system is effective in collecting class-specific information and has reliable robustness in managing view point changes like partial occlusion, rotation, zooming, and translation.