Language-based hand-printed character recognition: a novel method using spatial and temporal informative features

We propose a new method for recognition - the domain-dependent bilingual hand-printed character recognition. We implemented two-stage recognition systems based on two important character properties, defined as spatial and temporal informative features. The proposed spatial informative features (SIF) are off-line characters' structures that are exploited in order to differentiate Thai from English characters. These features can also be called distinctive features (DF). In contrast, temporal informative features (TIF) are segments of characters extracted using our proposed features, called start-to-end point distance feature, and other standard on-line features. Our proposed TIF features help us to solve ambiguity occurred in several Thai and English character, which conventional features cannot resolve. In the recognition system, the first stage is performed the language classification task using distinctive features, while the second stage is using hidden Markov model (HMM) as the final classifier. The advantages of using language classification at the first recognition stage are two folds. First, the decision complexity at the final recognition stage can be reduced. Second, the observation stages of two independent language HMMs can be better optimized than one bilingual HMM. From the experimental results, language classification recognition accuracy is 99.31%, while the recognition accuracy of Thai and English characters are 91.67% and 90.23%, respectively. Hence, the overall recognition accuracy is 91.05%.