Prohibition Signage Classification for the Visually Impaired Using AlexNet Transfer Learning Approach

Prohibition signs are commonly used for safety purposes in order to prevent and protect individuals from dangerous situations. These signs are placed in or around areas whereby they are clearly visible to the public. However, the visually impaired cannot visualize such signs. To help them, this paper proposes a system that combines Convolutional Neural Network (CNN) model and Computer Vision (CV) algorithms to detect and recognize prohibition signs in real scenes. The system uses pre-trained AlexNet model, fine-tuned using Prohibition Signage Boards (PSB) dataset and combined with Maximally Stable Extremal Regions (MSER) and Optical Character Recognition (OCR) techniques for text extraction and classification, to enhance the system performance. The experiments indicate that high recognition accuracies are achieved from a variety of prohibition images and prohibition texts.