An off-line oriental character recognition system (OOCRS): synergy of distortion modeling, hidden Markov models and vector quantization

Off-line handwritten oriental character recognition is a difficult task due to the large category and stroke variety. These oriental characters are made up of components known as radicals, which are often written in a distorted proportion and size. All these factors lead to a difficult recognition problem, which unfortunately cannot be solved using direct classification approach like the neural network classifier and a preprocessing module. This paper proposes several novel preprocessing approaches and synergy of classifiers to achieve good performance. Novel classification approaches, comprising rough and coarse classification modules are proposed which when combined appropriately produced a high-performance recognition system capable of producing high accuracy classification in off-line oriental character recognition. The recognition accuracy of the system is a high of 97% and a 99% for the top 5 candidate selection scores.