{'fraction_non_alphanumeric': 0.015367727771679473, 'fraction_numerical': 0.005488474204171241, 'mean_word_length': 4.96078431372549, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
An Efficient Representation of Shape for Object Recognition and Classification using Circular Shift Method

Object recognition/classification is a hugely researched domain in the areas of computer vision & image processing. The most valuable feature for object recognition is its shape, which is defined for 2D space. In which, a circular shift algorithm is used for finding the exact shape of the object. Finding an appropriate set of features is an essential problem in the design of object recognition system. Before going to recognize the object, first find the shape of the object then a K-nearest neighbor classifier is used for classification. The experimental result shows that the method for classification gives impressive results of above 96% when it was tested on Flavia dataset that contains 32 kinds of plant leaves. It means that the method gives better performance compared to the original work.