Multilingual Off-line Handwriting Recognition in Real-World Images Using Deep Neural Network (DNN) Classifier
Handwriting has continued to persist as a means of communication and recording information in day-to-day life even with the introduction of new technologies. Given its ubiquity in human transactions, machine recognition of handwriting has practical significance, as in reading handwritten notes in a Personal Digital Assistant (PDA), in postal addresses on envelopes, in amounts in bank checks, in handwritten fields in forms, etc. However the script-independent methodology for multilingual Offline Handwriting Recognition (OHR) becomes very difficult task, since the multilingual methods have different characters and words. Prediction of script-independent methodology reduces accuracy rate of the OHR method. To overcome this problem, new OHR of Tamil and English, how it is transduced into electronic data. It majorly focuses on the removal of noises and word, character segmentation methods with higher recognition rate. The images which are scanned may also contain noises. For image denoising steps consists of binarization, noise elimination, and size normalization. Words and characters segmentation are performed by using Particle Swarm Optimization (PSO) algorithm. Then those segmented samples are used for the next step which is feature extraction. Finally word recognition is performed by using the deep neural network classifier. The results shows that proposed method performs well compared to existing methods.
Author's Name: M. Sivasankari, Dr.P. Velmani and Dr.P. Arokia Jansi Rani