Hidden Markov Model with Parameter-Optimized K-Means Clustering for Handwriting Recognition

Handwriting recognition is a main topic of Optical Character Recognition (OCR), which has a very wide application area. Hidden Markov model is a popular model for handwriting recognition because of its effectiveness and robustness. In this paper, we propose a hidden Markov model with parameter-optimized k-means clustering for handwriting recognition. We explore two deep features of the images of characters, thus significantly boosting the effectiveness of k-means clustering. The experiments show that our model largely increases the average accuracy of HMM with k-means clustering to 83.5% when the number of clusters is 3000.