Title :
HMM-Based Online Recognition of Handwritten Chemical Symbols
Author :
Zhang, Yang ; Shi, Guangshun ; Yang, Jufeng
Author_Institution :
Inst. of Machine Intell., Nankai Univ., Tianjin, China
Abstract :
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.
Keywords :
chemical reactions; feature extraction; handwritten character recognition; hidden Markov models; image sampling; HMM-based online handwritten recognition method; academic interaction; chemical symbol; feature extraction; hidden Markov model; test sample; Character recognition; Chemical analysis; Feature extraction; Handwriting recognition; Hidden Markov models; Machine intelligence; Stochastic processes; Stochastic resonance; Text analysis; Writing; HMM; chemical symbols; online handwritten recognition;
Conference_Titel :
Document Analysis and Recognition, 2009. ICDAR '09. 10th International Conference on
Conference_Location :
Barcelona
Print_ISBN :
978-1-4244-4500-4
Electronic_ISBN :
1520-5363
DOI :
10.1109/ICDAR.2009.99
