Lottery Digit Recognition Based on Multi-features

In this paper, we propose a new method based on multi-features for lottery digit recognition Since the lottery digits are different from free handwritten ones, we can find a much more simple and reliable scheme to recognize them. Our proposed method is easier implemented than widely used neural network and support vector machine methods. Firstly, pre-processed isolated digit images are input for size normalization. Then image thinning and noise reduction are performed. Finally, the ending point, bifurcation, cross are detected easily. Our recognition system is two-staged. In the first stage, mainly using the number of ending point, the digits are classified into five classes. In the second stage, adopting multi-features such as freeman chain code, orientation information, likeness degree and so on, all the digits are recognized. Each lottery ticket contains 96 digits. The size of each digit image is 48x62 pixels, and the lottery database consists of 4800 digit patterns written by 50 people. The advantages of our method are that it does not require training, which can save a lot of time, and has slightly better recognition rate. Experimental results on this database demonstrate that the obtained recognition rate achieves 95%, which satisfies the lottery digit recognition rate, and multi-features always improve the classifier performance and reliability.