Threshold center-symmetric local binary convolutional neural networks for bilingual handwritten digit recognition

Arabic and English handwritten digit recognition is a challenging problem because the writing style differs from one writer to another. In middle east countries, many official forms are prepared to be written using either Arabic or English languages. However, some people fill the form using both languages (Arabic and English), which adds more challenges to recognize digits. Nowadays, deep learning approaches are considered the hot trend of new research, including Convolutional Neural Networks (CNN). CNN is used in many applications and modified to produce other models such as Local Binary Convolutional Neural Networks (LBCNN). LBCNN was created by fusing Local Binary Pattern (LBP) with CNN by reformulating LBP as a convolution layer called Local Binary Convolution (LBC). However, LBCNN suffers from the random assign 1, 0, or -1 to LBC weights, making LBCNN less robust. Nevertheless, using another LBP-based technique such as Center-Symmetric Local Binary Patterns (CS-LBP) can address such issues. In this thesis, a new model based on CS-LBP is proposed called Center-Symmetric Local Binary Convolutional Neural Networks (CS-LBCNN) that addresses the issues of LBCNN. Further, an enhanced version of CS-LBCNN is proposed called Threshold Center-Symmetric Local Binary Convolutional Neural Networks (TCSLBCNN) that addresses another issue related to the zero-thresholding function. The proposed models are compared against state-of-the-art techniques that used the MNIST and MADBase as a bilingual dataset. The proposed TCS-LBCNN model proves its ability to give a more accurate and significant classification rate than the existing LBCNN models. For the bilingual dataset, the TCS-LBCNN enhances the performance of LBCNN and CS-LBCNN, in terms of accuracy, by 0.15% and 0.03%, respectively. In addition, the comparison shows that the accuracy acquired by TCS-LBCNN is the second-highest using the MNIST and MADBase datasets

Pattern detection and recognition using over-complete and sparse representations

Recent research in harmonic analysis and mammalian vision systems has revealed that over-complete and sparse representations play an important role in visual information processing. The research on applying such representations to pattern recognition and detection problems has become an interesting field of study. The main contribution of this thesis is to propose two feature extraction strategies - the global strategy and the local strategy - to make use of these representations. In the global strategy, over-complete and sparse transformations are applied to the input pattern as a whole and features are extracted in the transformed domain. This strategy has been applied to the problems of rotation invariant texture classification and script identification, using the Ridgelet transform. Experimental results have shown that better performance has been achieved when compared with Gabor multi-channel filtering method and Wavelet based methods. The local strategy is divided into two stages. The first one is to analyze the local over-complete and sparse structure, where the input 2-D patterns are divided into patches and the local over-complete and sparse structure is learned from these patches using sparse approximation techniques. The second stage concerns the application of the local over-complete and sparse structure. For an object detection problem, we propose a sparsity testing technique, where a local over-complete and sparse structure is built to give sparse representations to the text patterns and non-sparse representations to other patterns. Object detection is achieved by identifying patterns that can be sparsely represented by the learned. structure. This technique has been applied. to detect texts in scene images with a recall rate of 75.23% (about 6% improvement compared with other works) and a precision rate of 67.64% (about 12% improvement). For applications like character or shape recognition, the learned over-complete and sparse structure is combined. with a Convolutional Neural Network (CNN). A second text detection method is proposed based on such a combination to further improve (about 11% higher compared with our first method based on sparsity testing) the accuracy of text detection in scene images. Finally, this method has been applied to handwritten Farsi numeral recognition, which has obtained a 99.22% recognition rate on the CENPARMI Database and a 99.5% recognition rate on the HODA Database. Meanwhile, a SVM with gradient features achieves recognition rates of 98.98% and 99.22% on these databases respectivel