Decision tree and deep learning based probabilistic model for character recognition

作     者：A.K.Sampath Dr.N.Gomathi 

作者机构：Rizvi College of EngineeringMumbaiMaharashtra 400050India Veltech Dr.R.R&Dr.S.R.Technical UniversityAvadi Chennai-600 062India 

出 版 物：《Journal of Central South University》 (中南大学学报（英文版）)

年 卷 期：2017年第24卷第12期

页      面：2862-2876页

核心收录：

学科分类：0810[工学-信息与通信工程] 0806[工学-冶金工程] 08[工学] 080203[工学-机械设计及理论] 0805[工学-材料科学与工程（可授工学、理学学位）] 0703[理学-化学] 0802[工学-机械工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：grey level co-occurrence matrix feature histogram oriented gabor gradient feature hybrid classifier holoentropy enabled decision tree classifier 

摘      要：One of the most important methods that finds usefulness in various applications, such as searching historical manuscripts, forensic search, bank check reading, mail sorting, book and handwritten notes transcription, is handwritten character recognition. The common issues in the character recognition are often due to different writing styles, orientation angle, size variation(regarding length and height), etc. This study presents a classification model using a hybrid classifier for the character recognition by combining holoentropy enabled decision tree(HDT) and deep neural network(DNN). In feature extraction, the local gradient features that include histogram oriented gabor feature and grid level feature, and grey level co-occurrence matrix(GLCM) features are extracted. Then, the extracted features are concatenated to encode shape, color, texture, local and statistical information, for the recognition of characters in the image by applying the extracted features to the hybrid classifier. In the experimental analysis, recognition accuracy of 96% is achieved. Thus, it can be suggested that the proposed model intends to provide more accurate character recognition rate compared to that of character recognition techniques used in the literature.