Artificial Intelligent Systems and Machine Learning

P. Banumathi ¹, G. M. Nasira ², B. Muthukumar ³

Affiliations
1 Dept. of CSE, Kathir College of Engg., Coimbatore, IN
2 Dept. of CS, Chikkanna Government Arts College, Tirupur, IN
3 Syed Ammal College of Engineering, Ramanathapuram, IN

Abstract

Textile industry is one of the main sources of revenue generating industry. The price of fabrics is severely affected by the defects of fabrics that represent a major threat to the textile industry. A very small percentage of defects are detected by the manual inspection even with highly trained, experienced inspectors. An automatic defect detection system can increase the defect detection percentage. It reduces the fabrication cost and economically profitable when we consider the labor cost and associated benefits. In this paper we have proposed a method to detect the defects in woven fabric based on the changes in the intensity of fabric. The images are acquired, preprocessed, statistical features based on the co-occurrence matrix and linear correlation coefficient using fast fourier transform are extracted. The Artificial Neural Network is used as classification model. The extracted features are given as input to the artificial neural network, it identifies the defect. The proposed method shows a better performance when compared with the existing methods.

Keywords

Artificial Neural Networks, Image Processing, Statistical Approach, Spectral Approach, Gray Level Co-Occurrence Matrix, Fourier Transform.

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G. M. Nasira ¹, P. Banumathi ²

Affiliations
1 Sasurie College of Engineering, Vijayamangalam, Tirupur (Dt), IN
2 Department of Computer Science & Engineering, Kathir College of Engineering, Neelambur, Coimbatore (Dt), IN

Abstract

Handwritten Character recognition is a process, which associates a symbolic meaning with letters, symbols and numbers drawn on an image. Many researches have been done to solve handwritten character recognition in the areas such as Image Processing, Pattern Recognition, and Artificial Intelligence etc. Recognition of offline handwritten character is a goal of much research effort in pattern recognition. Many techniques have been applied for recognition of handwritten characters but still it is the case of less efficiency and accuracy of recognition. Thus this paper brings out a complete system to recognize offline handwritten characters using Hidden Markov Model (HMM), in which an artificial neural network is trained to identify similarities and patterns among different handwritten samples with high accuracy. HMM has a Freedom to manipulate the training and verification processes. HMMs are very powerful modeling tools than many statistical methods.

Keywords

Artificial Neural Network (ANN), Feature Extraction, Handwritten Character Recognition, Hidden Markov Model (HMM), Preprocessing, Segmentation.

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