Reading signboards for the visually impaired using Pseudo-Zernike Moments. (July 2022)

• Record Type: Journal Article
• Authors: Guezouli, Larbi
• Is Part Of: Advances in engineering software. Volume 169(2022)
• Publication Date: 2022-07
• Subjects: Signboard localization -- Text detection -- Pseudo Zernike moments -- Image segmentation; Computer-aided engineering -- Periodicals; Engineering -- Computer programs -- Periodicals; Engineering -- Software -- Periodicals; Periodicals; 620.0028553
• DOI: 10.1016/j.advengsoft.2022.103127
• Languages: English
• ISSNs: 0965-9978

Abstract

Highlights: Using Pseudo-Zernike moments to detect the existence of a signboard. Using Pseudo-Zernike moments to filter text regions. Apply results to advanced Driver-Assistance Systems. Filtering of non-textual regions using the outline function. Abstract: It is necessary to provide essential daily support for the visually impaired, allowing them to locate themselves in an underground station, to change trains, to read signboards like those in doctors offices, law firms, shops, etc. In this paper, we present a Reading Signboards System (called RSbS) based on Pseudo Zernike Moments (PZMs) and stroke features from a video captured by a camera. The system is made up of three main steps: (i) signboard detection, (ii) text localization and (iii) filtering of non-textual regions. In a video, signboard region contains all the necessary information. Therefore, the signboard region should be located and segmented from the other parts of a frame that will be considered as the background. Signboards localization and segmentation are performed using Pseudo Zernike Moments. Text localization and extraction is done using PZMs segmentation which is based on the HSV color space and specifically on the Value channel. Non-textual regions will be filtered out using the outline function. System evaluation shows that it is robust to changes in lighting, low resolution and camera tilt, and it gives very few false positives compared to other approaches. In addition, the resulting images are transmitted to an optical character recognition engine (the best known is OCR) to extract the text which will be converted to speech using one of the existing APIs like Google Text to Speech API (gTTS API).