Towards Reliable Handwritten Character Recognition amid Diacritic Chaos

The orthographies of several languages, such as Yoruba, Arabic, Tshivenda, Ciluba, French, German, and Dutch, use diacritics. These diacritics pose an additional challenge to a character recognition system. Using diacritically-marked uppercase Yoruba letters as a case-study, this paper presents one strategy for addressing this problem. In particular, we present a system for the automatic classification of diacritically-marked handwritten uppercase Yorùbá letters in offline mode. Our approach involves six stages: a pre-processing stage; a segmentation stage for isolating the pertinent Latin letter from the diacritical mark(s); a feature extraction stage where eight geometric properties of the Latin letter are computed; a Bayesian classifier stage where the Latin letter is classified based on the extracted features; a decision tree stage where the diacritical marks are recognized; and a result fusion stage where the results of the two latter stages are combined into a single final class label. A recognition rate of 91.18% was obtained. Also, we introduce three new simple features and a new criterion for comparing the efficacies of features, and show that one of the three features we introduced outperforms four traditional simple moments based on the criterion we introduced.