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.

[1]  Jean-Yves Ramel,et al.  Automatic reading of handwritten chemical formulas from a structural representation of the image , 1999, Proceedings of the Fifth International Conference on Document Analysis and Recognition. ICDAR '99 (Cat. No.PR00318).

[2]  Guangshun Shi,et al.  Recognition of on-line handwritten chemical expressions , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[3]  Alejandro Héctor Toselli,et al.  Writing speed normalization for on-line handwritten text recognition , 2005, Eighth International Conference on Document Analysis and Recognition (ICDAR'05).

[4]  P. A. Chou,et al.  Recognition of Equations Using a Two-Dimensional Stochastic Context-Free Grammar , 1989, Other Conferences.

[5]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[6]  Jean Paul Haton,et al.  A Syntactic Approach for Handwritten Mathematical Formula Recognition , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Karl Zilles,et al.  Optical recognition of chemical graphics , 1993, Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR '93).

[8]  Zheng-Xing Sun,et al.  User-Independent Online Handwritten Digit Recognition , 2006, 2006 International Conference on Machine Learning and Cybernetics.

[9]  L. R. Rabiner,et al.  Recognition of isolated digits using hidden Markov models with continuous mixture densities , 1985, AT&T Technical Journal.

[10]  Shigeki Matsuda,et al.  Context-dependent substroke model for HMM-based on-line handwriting recognition , 2002, Proceedings Eighth International Workshop on Frontiers in Handwriting Recognition.

[11]  Alexander H. Waibel,et al.  Online handwriting recognition: the NPen++ recognizer , 2001, International Journal on Document Analysis and Recognition.