Fine-tuned CNN-based Sri Lankan Currency Note Detection Method for the Visually Impaired People Using Smartphones
A. S. K. Perera, G. S. N. Meedin · 2023
The currency has a significant meaning in everyday life. Researchers have suggested diverse approaches to improve currency recognition for visually impaired people by implementing portable devices and various image-processing techniques. However, only some of the nearly developed currency recognition systems cater to meet the Sri Lankan currency identification requirement. This paper presents a model that works on a mobile application based on a Convolutional Neural Network using a pre-trained model to identify currency notes in real time from a smartphone camera. Crucial factors in constructing this model are speed, accuracy, and size. A self-collected dataset of 14,000 Sri Lankan currency note images in numerous conditions was used for primary model training. Data augmentation, transfer learning through VGG16, VGG19, ResNet50, DenseNet, MobileNet, NASNet, and EfficientNet pre-trained models using bottleneck features and fine-tuning on top of bottleneck features are the main methods used in this model. This model reached above 98% in terms of testing accuracy using DenseNet121 with fine-tuning by testing 315 partially occulted, rotated Sri Lankan currency notes in different daylight conditions and cluttered backgrounds. The proposed model can recognize the currency note using TensorFlow Lite when it is pointed at the camera. The present research is limited to daylight image capturing to ensure the better performance of the model. The findings of this research would help visually impaired people to recognize Sri Lankan currency notes precisely and with less effort through the simple installation of a smartphone.