Handwritten Devanagari numeral recognition

Optical character recognition (OCR) plays a very vital role in today’s modern world. OCR can be useful for solving many complex problems and thus making human’s job easier. In OCR we give a scanned digital image or handwritten text as the input to the system. OCR can be used in postal department for sorting of the mails and in other offices. Much work has been done for English alphabets but now a day’s Indian script is an active area of interest for the researchers. Devanagari is on such Indian script. Research is going on for the recognition of alphabets but much less concentration is given on numerals. Here an attempt was made for the recognition of Devanagari numerals. The main part of any OCR system is the feature extraction part because more the features extracted more is the accuracy. Here two methods were used for the process of feature extraction. One of the method was moment based method. There are many moment based methods but we have preferred the Tchebichef moment. Tchebichef moment was preferred because of its better image representation capability. The second method was based on the contour curvature. Contour is a very important boundary feature used for finding similarity between shapes. After the process of feature extraction, the extracted feature has to be classified and for the same Artificial Neural Network (ANN) was used. There are many classifier but we preferred ANN because it is easy to handle and less error prone and apart from that its accuracy is much higher compared to other classifier. The classification was done individually with the two extracted features and finally the features were cascaded to increase the accuracy

Texture classification using discrete Tchebichef moments

In this paper, a method to characterize texture images based on discrete Tchebichef moments is presented. A global signature vector is derived from the moment matrix by taking into account both the magnitudes of the moments and their order. The performance of our method in several texture classification problems was compared with that achieved through other standard approaches. These include Haralick's gray-level co-occurrence matrices, Gabor filters, and local binary patterns. An extensive texture classification study was carried out by selecting images with different contents from the Brodatz, Outex, and VisTex databases. The results show that the proposed method is able to capture the essential information about texture, showing comparable or even higher performance than conventional procedures. Thus, it can be considered as an effective and competitive technique for texture characterization. © 2013 Optical Society of America.J. Víctor Marcos is a Juan de la Cierva research fellow funded by the Spanish Ministry of Economy and Competitiveness.Peer Reviewe

Robust face recognition using convolutional neural networks combined with Krawtchouk moments

Face recognition is a challenging task due to the complexity of pose variations, occlusion and the variety of face expressions performed by distinct subjects. Thus, many features have been proposed, however each feature has its own drawbacks. Therefore, in this paper, we propose a robust model called Krawtchouk moments convolutional neural networks (KMCNN) for face recognition. Our model is divided into two main steps. Firstly, we use 2D discrete orthogonal Krawtchouk moments to represent features. Then, we fed it into convolutional neural networks (CNN) for classification. The main goal of the proposed approach is to improve the classification accuracy of noisy grayscale face images. In fact, Krawtchouk moments are less sensitive to noisy effects. Moreover, they can extract pertinent features from an image using only low orders. To investigate the robustness of the proposed approach, two types of noise (salt and pepper and speckle) are added to three datasets (YaleB extended, our database of faces (ORL), and a subset of labeled faces in the wild (LFW)). Experimental results show that KMCNN is flexible and performs significantly better than using just CNN or when we combine it with other discrete moments such as Tchebichef, Hahn, Racah moments in most densities of noises

Feature Extraction Methods for Character Recognition

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On The Potential of Image Moments for Medical Diagnosis

Medical imaging is widely used for diagnosis and postoperative or post-therapy monitoring. The ever-increasing number of images produced has encouraged the introduction of automated methods to assist doctors or pathologists. In recent years, especially after the advent of convolutional neural networks, many researchers have focused on this approach, considering it to be the only method for diagnosis since it can perform a direct classification of images. However, many diagnostic systems still rely on handcrafted features to improve interpretability and limit resource consumption. In this work, we focused our efforts on orthogonal moments, first by providing an overview and taxonomy of their macrocategories and then by analysing their classification performance on very different medical tasks represented by four public benchmark data sets. The results confirmed that convolutional neural networks achieved excellent performance on all tasks. Despite being composed of much fewer features than those extracted by the networks, orthogonal moments proved to be competitive with them, showing comparable and, in some cases, better performance. In addition, Cartesian and harmonic categories provided a very low standard deviation, proving their robustness in medical diagnostic tasks. We strongly believe that the integration of the studied orthogonal moments can lead to more robust and reliable diagnostic systems, considering the performance obtained and the low variation of the results. Finally, since they have been shown to be effective on both magnetic resonance and computed tomography images, they can be easily extended to other imaging techniques

Fast Computation of Sliding Discrete Tchebichef Moments and Its Application in Duplicated Regions Detection

International audienceComputational load remains a major concern when processing signals by means of sliding transforms. In this paper, we present an efficient algorithm for the fast computation of one-dimensional and two-dimensional sliding discrete Tchebichef moments. To do so, we first establish the relationships that exist between the Tchebichef moments of two neighboring windows taking advantage of Tchebichef polynomials’ properties. We then propose an original way to fast compute the moments of one window by utilizing the moment values of its previous window. We further theoretically establish the complexity of our fast algorithm and illustrate its interest within the framework of digital forensics and more precisely the detection of duplicated regions in an audio signal or an image. Our algorithm is used to extract local features of such a signal tampering. Experimental results show that its complexity is independent of the window size, validating the theory. They also exhibit that our algorithm is suitable to digital forensics and beyond to any applications based on sliding Tchebichef moments