Off-line character recognition using HMM by multiple directional feature extraction and voting with bagging algorithm

The purpose of our research is to improve the recognition rate of offline character recognition systems using the HMM (Hidden Markov Model) without increasing the number of HMM parameters too much. Some 2-dimensional HMM character recognition systems have been proposed to increase representational power. However since 2D HMM has a much more complex structure and thus requires much more parameters than 1-dimensional HMM, it becomes very hard to gather sufficient samples in order to guarantee successful generalization. To overcome the problem, we propose a method for character recognition using 1D HMMs in multiple directions with 2-dimensional feature extraction. To further improve the performance, some voting methods using a bagging algorithm are also exploited. In our experiment, the recognition rate is increased by about 1% with the multiple directional HMM character recognition system compared to the 1D HMM character recognition system. The recognition rate is further increased by about 1% with the HMM character recognition system using a bagging algorithm