Decision tree and deep learning based probabilistic model for character recognition

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...

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Bibliographic Details
Published in:Journal of Central South University 2017-12, Vol.24 (12), p.2862-2876
Main Authors: Sampath, A. K., Gomathi, Dr. N.
Format: Article
Language:English
Subjects:
Artificial neural networks
Character recognition
Classifiers
Decision trees
Deep learning
Engineering
Feature extraction
Forensic engineering
Handwriting
Handwriting recognition
Metallic Materials
Neural networks
Object recognition
OCR
Probabilistic models
Shape recognition
Statistical analysis
Texture recognition
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Summary: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.
ISSN:2095-2899
2227-5223
DOI:10.1007/s11771-017-3701-8