MULTI-ORIENTED TEXT DETECTION IN SCENE IMAGES
We present a new run-length based method for multi-oriented text detection in scene images. We consider one ideal Sobel edge image of the horizontal text image to compute run-lengths for multi-oriented text images. Then the method proposes a Max-Min clustering to find ideal run-lengths that represents text pixel from an array of run-lengths of ideal image. The run-lengths computed for the input multi-oriented and horizontal text images are matched with the ideal run-lengths given by the Max-Min clustering to find potential run-lengths. The boundary growing method is introduced to traverse multi-oriented text lines given by the potential run-lengths and then the method eliminates false positives to clear the background using angle-proximity features of the text blocks. The non-horizontal text image is rotated to horizontal direction based on angle of the text lines to ease the implementation. The method explore new idea based on zero-crossing to separate text lines from the touching text lines given by the boundary growing method. The proposed method is tested on our own multi-oriented scene data captured by high resolution camera and mobile camera, and the benchmark database (ICDAR 2003 competition scene images) to evaluate the performance of the proposed method. The
multi-oriented text run-lengths for multi-oriented text boundary growing zero crossing scene text detection