Store Sign Text Recognition for Wearable Navigation Assistance System

Abstract

With the development of computer vision, wearable computing technologies not only have changed our lifestyle, but also have provided much convenience for vulnerable road users, especially the Visually Impaired (VI) pedestrians. VI people have difficulties in locating and socializing due to the limitations of traditional assistive tools, e.g., the inability to recognize text. Text plays a significant role in various aspects, which can convey abundant semantic information of the scene. In recent years, text detection and recognition has made huge progress which makes it possible for VI people to understand the surroundings by using scene text information. In this paper, a text recognition system is proposed to help VI people to perceive store sign text. Firstly, we locate the text on the sign with the aim of leading VI pedestrian to reach the destination store. Towards this end, an objection detection network is integrated into the system to extract Regions of Interest (ROI) in complex real-world scenarios. In order to fulfil real-time assistance, an efficient detection network named Single Shot MultiBox Detector (SSD) has been made light-weight and embedded in the wearable system. Secondly, we leverage an open-source optical character recognition (OCR) instrument to recognize the detected text. Afterwards, we introduce the collected dataset and critical training tips for the task. Finally, a comprehensive set of experiments on our dataset demonstrates that our approach significantly improves the precision and make the recognition robust even in real-world settings. Based on our approach, the wearable system can feedback the recognized text in real time and assist the VI people during their every independent navigation.