Handwritten digits recognition using dictionary learning

Handwritten documents have been a valuable resource in human transactions for many years. Today, there is an immediate need for computer-based techniques to intelligently read and analyze such documents. Meanwhile, handwritten numerals are of particular importance due to their role in finance, business, post, etc. Although there exist many researches on English handwritten number recognition, the development of reliable recognition systems has been paid little attention for non-English scripts. In this chapter, an overview of the state-of-the-art on handwritten number recognition (with focus on non-English languages) is presented. Dictionary learning as a supervised learning technique, which has been recently shown great success in image classification problems, is introduced. We describe the ways one can design discriminative dictionaries for classification of handwritten numbers. The obtained dictionaries convey exclusive features of the associated numerals. In order to improve the classification performance of handwritten numbers using dictionary learning, two novel approaches are presented. First, an incoherence penalty is combined with the learning process to fine-tune the structure of the dictionaries learned for each class. Second, class label information is embedded into the learning process in order to produce class-specific weights which improve the discriminativity of the learned dictionaries. We further adopt a new feature space, that is, histogram of oriented gradients (HOG) to generate the dictionary atoms. HOG is a strong descriptor of most handwritten images especially those studied in this chapter. Four different handwritings, namely, Chinese, Persian, Arabic, as well as English are used to evaluate the performance of the proposed methods. We also present a convolutional neural network model to compare the performance of deep learning with that of dictionary learning for handwritten digits recognition. The obtained results and their comparisons with benchmark methods confirm the effectiveness and robustness of the proposed approaches for recognition of handwritten numbers.