A New Approach for Text String Detection from Natural Scenes By Grouping & Partition

Abstract

In this paper we have reviewed and analyzed different methods to find strings of characters from natural scene images. We have reviewed different techniques like extraction of character string regions from scenery images based on contours and thickness of characters, efficient binarization and enhancement technique followed by a suitable connected component analysis procedure, text string detection from natural scenes by structure - based partition and grouping, and a robust algorithm for text detection in images. It is assumed that characters have closed contours, and a character string consists of characters which lie on a straight line in most cases. Therefore, by extracting closed contours and searching neighbors of them, character string regions can be extracted; Image binarization successfully processed natural scene images having shadows, non - uniform illumination, low contrast and large signal - dependent noise. Connected component analysis is used to define the final binary images that mainly consist of text regions. One technique chooses the candidate text characters from connected components by gradient feature and color feature. The text line grouping method performs Hough transform to fit text line among the centroids of text candidates. Each fitte d text line describes the orientation of a potential text string. The detected text string is presented by a rectangle region coveri ng all characters whose centroids are cascaded in its text line. To improve efficiency and accuracy, our algorithms are carried out in multi - scales. The proposed methods outperform the state - of - the - art results on the public Robust Reading Dataset, which contains text only in horizontal orientation. Furthermore, the effectiveness of our methods to detect text strings with arbitrary orientations is evaluated on the Oriented Scene Text Dataset collected by ourselves containing text strings in no horizontal orientations.