Tübıtak Turkish — Ottoman handwritten recognition system
M. Said Aydemir, Burak Aydin, Hamza Kaya, Ibrahim Karliaga, Cemil Demir · 2014
In this study, two different Ottoman and Turkish handwritten recognition systems have been developed using Hidden Markov Model (HMM) and Recurrent Neural Network (RNN). The systems are tested in both public use datasets and Civil Registration and Nationality (CRN) dataset. As public use datasets, IFN/ENIT dataset which is created for Arabic language, is used because of the similarity between Ottoman and Arabic, IAM dataset is tested which consists of Latin characters. Because of the CRN dataset is not suitable for direct usage, contrast enhancement, line and background destruction, converting 24 bit image to binary format, image resize for normalized font value, skew detection and correction are applied as pre-processing steps. When the recognition results of both systems are compared, the system which employs the RNN gives %8 higher accuracy then system which employs HMM.