A Novel Framework For Text Detection From Natural Scene Images With Complex Background

TL;DR

This paper introduces a wavelet transform-based framework for detecting text in natural scene images with complex backgrounds, offering a generalized and efficient solution that does not depend on specific font sizes.

Contribution

The proposed method uniquely combines wavelet transforms and region clustering to improve text detection accuracy in complex backgrounds, surpassing previous approaches.

Findings

Effective detection of text regions in images with complex backgrounds.

Method is adaptable to different applications and font sizes.

Performed well on a set of 50 diverse images.

Abstract

Recognizing texts from camera images is a known hard problem because of the difficulties in text detection from the varied and complicated background. In this paper we propose a novel and efficient method to detect text region from images with complex background using Wavelet Transforms. The framework uses Wavelet Transformation of the original image in its grayscale form followed by Sub-band filtering. Then Region clustering technique is applied using centroids of the regions, further Bounding box is fitted to each region thus identifying the text regions. This method is much sophisticated and efficient than the previous methods as it doesn't stick to a particular font size of the text thus, making it generalized. The sample set used for experimental purpose consists of 50 images with varying backgrounds. Images with edge prominence are considered. Furthermore, our method can be easily customized for applications with different scopes.