An MLP-SVM combination architecture for offline handwritten digit recognition

Abstract.

This paper presents an original hybrid MLP-SVM method for unconstrained handwritten digits recognition. Specialized Support Vector Machines (SVMs) are introduced to improve significantly the multilayer perceptron (MLP) performance in local areas around the separating surfaces between each pair of digit classes, in the input pattern space. This hybrid architecture is based on the idea that the correct digit class almost systematically belongs to the two maximum MLP outputs and that some pairs of digit classes constitute the majority of MLP substitutions (errors). Specialized local SVMs are introduced to detect the correct class among these two classification hypotheses. The hybrid MLP-SVM recognizer achieves a recognition rate of $98.01\%$, for real mail zipcode digits recognition task. By introducing a rejection mechanism based on the distances provided by the local SVMs, the error/reject trade-off performance of our recognition system is better than several classifiers reported in recent research.