Video Character Recognition through Hierarchical Classification

We present a new video character recognition method based on hierarchical classification. In the first step, we propose a method for character segmentation of the text line detected by the text detection method. The segmentation algorithm uses dynamic programming to find least-cost paths in the gray domain to identify the spaces between characters. For the segmented characters, we get a Canny edge image as input for the character recognition step. We introduce hierarchical classification based on voting criteria with structural features to classify 62 character classes into different smaller classes. We divide the perimeter of a character into 8 segments according to 8 directions at the centroid. Then the shape of each segment is studied to recognize the characters based on distances between the centroid and end points, and distances between the midpoint and end points. Our experiments on 1462 characters of upper case, lower case and numerals shows that 10% samples per class for training is enough to obtain 94.5% recognition accuracy. The dataset is chosen from TRECVID database of 2005 and 2006.