Mixed handwritten and printed digit recognition in Sudoku with Convolutional Deep Belief Network

Abstract

In this paper, we propose a method to recognize Sudoku puzzles containing both handwritten and printed digits from images taken with a mobile camera. The grid and the digits are detected using various image processing techniques including Hough Transform and Contour Detection. A Convolutional Deep Belief Network is then used to extract high-level features from raw pixels. The features are finally classified using a Support Vector Machine. One of the scientific question addressed here is about the capability of the Deep Belief Network to learn extracting features on mixed inputs, printed and handwritten. The system is thoroughly tested on a set of 200 Sudoku images captured with smartphone cameras under varying conditions, e.g. distortion and shadows. The system shows promising results with 92% of the cells correctly classified. When cell detection errors are not taken into account, the cell recognition accuracy increases to 97.7%. Interestingly, the Deep Belief Network is able to handle the complex conditions often present on images taken with phone cameras and the complexity of mixed printed and handwritten digits.