HMM-Based Online Recognition of Handwritten Chemical Symbols

In this paper, we present an online handwritten recognition method for Chemical Symbols, a widely used symbol in education and academic interactions. This method is based on Hidden Markov Models (HMMs), which are increasingly being used to model characters. We built an HMM for each symbol and used 11-dimensional local features which are suitable for online handwritten recognition, and obtained top-1 accuracy of 89.5% and top-3 accuracy of 98.7% on a dataset containing 5,670 train samples and 2,016 test samples. These initial results are promising and warrant further research in this direction.

• Type: Journal
• Year: 2009
• Where: ICDAR
• Authors: Yang Zhang, Guangshun Shi, Jufeng Yang
• Keywords: 11-dimensional Local Features, Document Analysis, Hidden Markov Models, ICDAR 2009, Online Handwritten Recognition