Automatic Identification and Skew Estimation of Text Lines in Real Scene Images

The paper presents an algorithm for the automatic localization of text embedded in complex images. It detects the spatial position and the skew of the text lines which are present in the scene and returns a binary representation of each text line. For comparison reasons we report a brief review of recent works in this field. Strength of the presented algorithm are independence of text skew and of presence of connected text. Two main assumptions are made about text lines: to have a straight base line, to be composed of characters with comparable heights. After a pre-processing step the input image is segmented in order to obtain a set of connected components which represent the basic elements of the algorithm. Several heuristics are proposed to characterized text components with respect to non-text ones: they depend both on the geometrical features of single components and on the geometrical and spatial relations among components. According to these heuristics several components are discarded and the retained ones are grouped into text lines candidates by means of a divisive hierarchical clustering procedure. In the experimental session we describe the application of the algorithm to the extraction of text lines from images of 100 book covers. Results about skew estimation are also reported.