Persian Handwritten Digit Recognition with Classifier Fusion: Class Conscious versus Class Indifferent Approaches

A large experiment on Persian handwritten digits are reported and discussed. In this paper the techniques to combine multiple classifiers based on static structures is investigated. A static structure includes two main strategies to combine result of base classifiers: a) class indifferent methods b) class conscious methods. We establish our model on Decision Template and Dempster Shafer, which are under category of class indifferent method, and compare theirs recognition rate with five of the most famous combining methods of class conscious category. To evaluate our proposed model a real-world database of Persian handwritten digits containing 8600 handwritten digit images is used. Experiments using our database demonstrate that combining result of base classifiers with class indifferent methods indeed are far more effective than combining the result with class conscious methods in Persian handwritten digit recognition. Evaluating the proposed system with 2150 test samples the recognition rate of 91.98% is achieved.