Virtual Assistant and Navigation for Visually Impaired using Deep Neural Network and Image Processing

Abstract

The quick development of technology promotes the use of the resources that are at hand to make simpler daily tasks and improve the standard and quality of life for those who are blind. This module suggests creating a system of virtual assistant glasses to help the visually impaired navigate around them. An obstacle detection module built into the device uses computer vision to find obstacles and inform the user through haptic feedback. The system also has a text recognition module that can turn any text it identifies into speech that the user can hear using built-in speakers in the user's glasses. Users are now able to access to printed materials like menus and signs in a way that was not previously feasible. A sign board recognition module, which converts text on signs into speech, is also part of the system. The proposed approach has the potential to enhance the liberty and standard of existence of blind individuals through integrating these attributes in a wearable device.

WSREB Mechanism: Web Search Results Exploration Mechanism for Blind Users

“…They used contextual information such as data types, captions, matching sentences, etc., and devised a keyboard-based navigational menu for interaction. Bukhaya et al [22], Nair et al [23], and Christopherson et al [24] leveraged image processing techniques via deep learning to convert the visual information into text for subsequent processing by a text reader. Tucket et al [25] embedded Near Field Connectivity (NFC) in academic pages preloaded with the speak command.…”

“…Approach Limitations [10] BUs face challenges due to lack of navigational support Reliance on third-party assistive tools [11] Utilization of screen readers, JAWS, voice assistants Limited effectiveness of assistive tools [20] Voice-activated email prototype Focused on a specific application (email) [21] Reduce cognitive load using summarized information Limited to information presented in tabular format [22][23][24] Leverage image processing to convert visual information Relies on image recognition; may not cover all content [25] Embed Near Field Connectivity (NFC) for interaction Limited to specific contexts (academic pages) [26] Pigeon algorithm for efficient web page retrieval Focused on improving search result relevancy [27] Image processing for automatic graph description Limited to content with graphical elements [28] Voice assistance augmentation for BUs Primarily extends voice assistant functionality…”