An Attention-Based Deep Learning System for Text Detection and Information Retrieval from Images

Abstract

This paper presents a new automatic approach to the extraction of textual information from natural photographs, addressing issues such as cluttered backgrounds, uneven lighting, and distortions. The system combines advanced computer vision techniques with OCR to detect, extract, and process text embedded in complex scenes. A deep learning-based text detection module identifies the text regions with high precision, and a robust OCR pipeline is used for extracting the detected text into a machine-readable form.
Experimental evaluations indicate how well the system works under practical real-world applications, including obtaining an average text detection precision of 92% and accuracy for character recognition to be about 88%. Broad applications for this proposed method involve real-time translation, augmented reality, tools on accessibility for visually impaired persons, intelligent traffic monitoring, and even automated content indexing. It makes a new standard for information retrieval systems by being able to correctly extract text from natural scenes and opens the gates to innovative applications in artificial intelligence and digital data processing.