This paper presents the project "edugame" whose objective is to provide an educational game which introduces a series of activities such as the construction of words, the recognition of letters or mathematical operations for teaching children. We chose to follow, for this project, a methodological approach which begins with a design phase, using UML, followed by an implementation phase using the Android platform. This allowed us to make available to children of 3 to 7 years an interactive game for independent learning.
This research presents a comparison between two methods of learning-classification, the first one is a probabilistic and unsupervised which is the Hidden Model Markov (HMM), while the second is a statistic and supervised that is the Support Vectors Machine (SVM). These techniques are used for printed Eastern Arabic numerals recognition, in different situation: rotated and translated or resized and noisy. In the pre-processing phase we have used the thresholding technique while in the features extraction we exploited the Krawtchouk Invariant Moment (KIM). In fact, in order to make a precise comparison between these two methods, we have introduced two new concepts which are the threshold and the interval of stability of each numeral and for each of these two methods. The simulation results that we obtained demonstrates that SVM is more performing than the HMM technique in this recognition system.
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