Exploring Text Recognition Segmentation and Detection in Natural Scene Images

Abstract

Identification, segmentation, and recognition of fonts from real-world images are major challenges in computer vision, particularly due to subtle differences in font shapes, lighting, and backgrounds. This paper aims to provide a comprehensive review of the latest algorithms for text detection, segmentation, and recognition from natural scene images. A variety of
techniques are assessed for their use in natural settings, including deep learning-based methods, region proposal, and feature-based detection. There is additional discussion of the difficulties of managing changes in text properties such as font type, size, orientation, and noise and occlusion disruptions. This survey also looks at preprocessing techniques like filtering and
illumination normalization that are meant to increase the accuracy of text detection. In light of the findings of the literature analysis, this study concludes that the combination of adaptive segmentation techniques with deep learning-based recognition models offers promising performance in text recognition in natural scenery images. This survey provides a foundation
for the development of more effective and robust methods for future applications in the fields of image processing and computer vision.