Deep Learning Techniques for Detecting and Segmenting Text in Natural Scene Images: Review

Abstract

Text detection and segmentation in natural scene images is an active research problem in computer vision and document analysis. Unlike scanned documents, scene text exhibits significant diversity in appearance, orientation, scale, font, and lighting conditions. In this review, survey the current state-of-the-art in techniques and methodologies aimed at detecting and segmenting text regions from images of natural scenes are presented. Both traditional approaches using hand-crafted features as well as modern data-driven deep learning methods will be discussed. The review will analyze common datasets, evaluation protocols and metrics used for benchmarking. Limitations of existing methods and open challenges in handling multilingual text, curved text, and efficiency will be highlighted. Promising future directions towards robust and generalizable scene text extraction systems will be identified. In summary, the review will provide a comprehensive overview of the advances, remaining challenges and future opportunities in developing automated systems for detecting and segmenting text in unconstrained natural images.