An automatic image description generation technology and application for visually impaired individuals

For visually impaired individuals, the ability to receive real-time textual descriptions of image content can significantly enhance their quality of life and learning. In daily life, automatically generated image descriptions can help them quickly acquire key information, improving their responsiveness and communication experience. In education, particularly in subjects involving complex images or spatial understanding, descriptive content can aid in better comprehension of study materials, thereby narrowing the learning gap between visually impaired students and their peers, and promoting educational equity. This study has developed an automated image description generation system for visually impaired individuals, leveraging deep learning models to accurately diagnose and describe image content. The system employs a combination of Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTM), integrating attention mechanisms to significantly enhance the quality and performance of the generated descriptions. Trained on large-scale image datasets such as CIFAR-100, Flickr8k and MS COCO, the system can accurately recognize image content and produce corresponding descriptions. Experimental results demonstrate that the proposed system outperforms traditional methods across multiple datasets, generating descriptions with high accuracy and fluency. This paper offers an in-depth description of the system’s design and execution, covering the design of the system architecture, dataset selection and preprocessing, model training and optimization and system testing and evaluation. The research results showcase the broad application prospects of automated image description technology in visual aid devices and automated news generation systems, offering valuable references for future technical optimization and application expansion.