Year: 2024
Type: article
Abstract: This study introduces a solution that applies document analysis and recognition technologies to enhance document accessibility for individuals with visual impairments. The objective is to develop an algorithm capable of accurately analyzing the content of document components and converting them into voice format. Leveraging the pre-trained YOLOv8 model for document analysis and optical character recognition technology, the image annotation model uses the AIAnytime API and Pix2Tex technology to extract LaTeX code from images, facilitating the conversion of mathematical formulas into spoken words. The research results demonstrate significant progress in effectively supporting document reading, making a meaningful contribution to the field of assistive technology for the visually impaired.
Source: Tạp chí Khoa học
Authors: Thái Thị Kim Yến, Nguyễn Thị Thu Hà, Võ Thị Quế Trân, Huỳnh Ngô Mỹ Vy, Trần Hoàng Yến Nhi, Ngô Quốc Việt
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
SDG: Reduced inequalities
Open Access status: diamond
OpenAlex ID: W4403846899