Locating text in images using matched wavelets

Abstract

In this paper we have proposed a novel scheme for locating text regions in an image. The method is based on multiresolution wavelet analysis. We used matched wavelets to capture textural characteristics of image regions. A clustering based approach has been proposed for estimating globally matched wavelets (GMWs) for a given collection of images. Using these GMWs, we generate feature vectors for segmentation and identification of text regions in an image. Our method, unlike most of the other methods, does not require any a priori information about the font, fontsize, scripts, geometric transformation, distortion or background texture. We have tested our method on various categories of images like license plates, posters, hand written documents and document images etc. The results show proposed method to be a robust, versatile and effective tool for text extraction from images.

A New Method for 2D-Adapted Wavelet Construction: An Application in Mass-Type Anomalies Localization in Mammographic Images

“…Kumar et al [27] propose the extraction of regions with text from an image using adapted wavelet filters and texture features in the image. Unlike other methods, this one does not require a priori information about the font, geometric transformation, distortion or, texture of the background.…”