Learning-Based Number Recognition on Spiral Architecture

In this paper, a number recognition algorithm is proposed on spiral architecture, a hexagonal image structure. This algorithm employs RULES-3 inductive learning method to recognize numbers. The algorithm starts from a collection of samples of numbers from number plates. Edge maps of the samples are then detected based on spiral architecture. A set of rules are extracted using these samples by RULES-3. The rules describe the frequencies of 9 different edge masks appearing in the samples. Each mask is a cluster of 7 hexagonal pixels. In order to recognize a number plate, all numbers are tested one by one using the extracted rules. The number recognition is achieved by counting the frequencies of the 9 masks. In this paper, a comparison between results based on rectangular structure and the results based on spiral architecture is given. From the experimental results, we can make the conclusion that Spiral Architecture is better than rectangular structure for inductive learning-based number recognition