High-Quality Wavelets Features Extraction for Handwritten Arabic Numerals Recognition

Arabic handwritten digit recognition is the science of recognition and classification of handwritten Arabic digits. It has been a subject of research for many years with rich literature available on the subject.  Handwritten digits written by different people are not of the same size, thickness, style, position or orientation. Hence, many different challenges have to overcome for resolving the problem of handwritten digit recognition.  The variation in the digits is due to the writing styles of different people which can differ significantly.  Automatic handwritten digit recognition has wide application such as automatic processing of bank cheques, postal addresses, and tax forms. A typical handwritten digit recognition application consists of three main stages namely features extraction, features selection, and classification. One of the most important problems is feature extraction. In this paper, a novel feature extraction approach for off-line handwritten digit recognition is presented. Wavelets-based analysis of image data is carried out for feature extraction, and then classification is performed using various classifiers. To further reduce the size of training data-set, high entropy subbands are selected. To increase the recognition rate, individual subbands providing high classification accuracies are selected from the over-complete tree. The features extracted are also normalized to standardize the range of independent variables before providing them to the classifier. Classification is carried out using k-NN and SVMs. The results show that the quality of extracted features is high as almost equivalently high classification accuracies are acquired for both classifiers, i.e. k-NNs and SVMs

Fusions of CNN and SVM Classifiers for Recognizing Handwritten Characters

© Xiaoxiao Niu, 2011 CONCORDIA UNIVERSITY School of Graduate Studies This is to certify that the thesis prepare

Two Novel Learning-Based Criteria and Methods Based on Multiple Classifiers for Rejecting Poor Handwritten Digits

In pattern recognition, the reliability and the recognition accuracy of a classification system are of same importance, because even a small percentage of errors could cause a huge loss in real-life handwritten numeral recognition systems, like cheque-reading at financial institutions. Aiming at improving the reliability of recognition systems, this thesis presents two novel learning-based rejection criteria for single classifiers including SVM-based measurement (SVMM) and Area Under the Curve measurement (AUCM). Voting based combination methods of multiple classifier system (MCS) are also proposed for rejecting poor handwritten digits. Different rejection criteria (FRM, FTRM and SVMM) are individually combined with MCSs as weight parameters in voting. This method is then evaluated on three renowned databases including MNIST, CENPARMI and USPS. Experimental results indicate that these combinations improve the rejection performances consistently. To further improve the performance of the MCS based rejection method, specialist information has been integrated into the combination process by introducing a new confidence weight parameter. The best result on MNIST is obtained by the simpler one of the two proposed methods of deriving this parameter, which reaches 100% reliability with a rejection rate of only 4.09%, the best value in this field

Labeled projective dictionary pair learning: application to handwritten numbers recognition

Dictionary learning was introduced for sparse image representation. Today, it is a cornerstone of image classification. We propose a novel dictionary learning method to recognise images of handwritten numbers. Our focus is to maximise the sparse-representation and discrimination power of the class-specific dictionaries. We, for the first time, adopt a new feature space, i.e., histogram of oriented gradients (HOG), to generate dictionary columns (atoms). The HOG features robustly describe fine details of hand-writings. We design an objective function followed by a minimisation technique to simultaneously incorporate these features. The proposed cost function benefits from a novel class-label penalty term constraining the associated minimisation approach to obtain class-specific dictionaries. The results of applying the proposed method on various handwritten image databases in three different languages show enhanced classification performance (~98%) compared to other relevant methods. Moreover, we show that combination of HOG features with dictionary learning enhances the accuracy by 11% compared to when raw data are used. Finally, we demonstrate that our proposed approach achieves comparable results to that of existing deep learning models under the same experimental conditions but with a fraction of parameters

Reconnaissance de l'écriture manuscrite en-ligne par approche combinant systèmes à vastes marges et modèles de Markov cachés

Handwriting recognition is one of the leading applications of pattern recognition and machine learning. Despite having some limitations, handwriting recognition systems have been used as an input method of many electronic devices and helps in the automation of many manual tasks requiring processing of handwriting images. In general, a handwriting recognition system comprises three functional components; preprocessing, recognition and post-processing. There have been improvements made within each component in the system. However, to further open the avenues of expanding its applications, specific improvements need to be made in the recognition capability of the system. Hidden Markov Model (HMM) has been the dominant methods of recognition in handwriting recognition in offline and online systems. However, the use of Gaussian observation densities in HMM and representational model for word modeling often does not lead to good classification. Hybrid of Neural Network (NN) and HMM later improves word recognition by taking advantage of NN discriminative property and HMM representational capability. However, the use of NN does not optimize recognition capability as the use of Empirical Risk minimization (ERM) principle in its training leads to poor generalization. In this thesis, we focus on improving the recognition capability of a cursive online handwritten word recognition system by using an emerging method in machine learning, the support vector machine (SVM). We first evaluated SVM in isolated character recognition environment using IRONOFF and UNIPEN character databases. SVM, by its use of principle of structural risk minimization (SRM) have allowed simultaneous optimization of representational and discriminative capability of the character recognizer. We finally demonstrate the various practical issues in using SVM within a hybrid setting with HMM. In addition, we tested the hybrid system on the IRONOFF word database and obtained favourable results.Nos travaux concernent la reconnaissance de l'écriture manuscrite qui est l'un des domaines de prédilection pour la reconnaissance des formes et les algorithmes d'apprentissage. Dans le domaine de l'écriture en-ligne, les applications concernent tous les dispositifs de saisie permettant à un usager de communiquer de façon transparente avec les systèmes d'information. Dans ce cadre, nos travaux apportent une contribution pour proposer une nouvelle architecture de reconnaissance de mots manuscrits sans contrainte de style. Celle-ci se situe dans la famille des approches hybrides locale/globale où le paradigme de la segmentation/reconnaissance va se trouver résolu par la complémentarité d'un système de reconnaissance de type discriminant agissant au niveau caractère et d'un système par approche modèle pour superviser le niveau global. Nos choix se sont portés sur des Séparateurs à Vastes Marges (SVM) pour le classifieur de caractères et sur des algorithmes de programmation dynamique, issus d'une modélisation par Modèles de Markov Cachés (HMM). Cette combinaison SVM/HMM est unique dans le domaine de la reconnaissance de l'écriture manuscrite. Des expérimentations ont été menées, d'abord dans un cadre de reconnaissance de caractères isolés puis sur la base IRONOFF de mots cursifs. Elles ont montré la supériorité des approches SVM par rapport aux solutions à bases de réseaux de neurones à convolutions (Time Delay Neural Network) que nous avions développées précédemment, et leur bon comportement en situation de reconnaissance de mots

Detección de fibrilación auricular en señales ECG usando Redes Neuronales para pacientes específicos

Atrial Fibrillation (AF) is the most common cardiac arrhythmia worldwide. It is associated with reduced quality of life and increases the risk of stroke and myocardial infarction. Unfortunately, many cases of AF are asymptomatic and undiagnosed, which increases the risk for the patients. Due to its paroxysmal nature, the detection of AF requires the evaluation, by a cardiologist, of long-term ECG signals. In Colombia, it is difficult to have access to an early diagnosis of AF because of the associated costs to the detection and the geographical distribution of cardiologists. This work is part of a macro project that aims to develop a specific-patient portable device for the detection of AF. This device will be based on a Convolutional Neural Network (CNN). We aim to find a suitable CNN model, which later could be implemented in hardware. Diverse techniques were applied to improve the answer regarding accuracy, sensitivity, specificity, and precision. The final model achieves an accuracy of , a specificity of , a sensitivity of  and a precision of . During the development of the model, the computational cost and memory resources were taking into account in order to obtain an efficient hardware model in a future implementation of the device.La fibrilación auricular (FA) es la arritmia cardíaca más común en todo el mundo. Se asocia con una reducción de la calidad de vida y aumenta el riesgo de accidente cerebrovascular e infarto de miocardio. Desafortunadamente muchos casos de FA son asintomáticos, lo cual aumenta el riesgo para los pacientes. Debido a su naturaleza paroxística, la detección de la FA requiere la evaluación, por parte de un cardiologo, de señales ECG de larda duración. En Colombia, es difícil dificil tener dianóstico temprano de la FA debido a los costos asociados a la detección de la FA y la distribución geográfica de los cardiólogos. Este trabajo es parte de un proyecto macro que tiene como objetivo desarrollar un dispositivo portátil para pacientes específicos que permita detectar la FA, el cual estará basado en una red neuronal convolucional (CNN). Nuestro objetivo es encontrar un modelo CNN adecuado, que luego se pueda implementar en hardware. Se aplicaron diversas técnicas para mejorar la respuesta con respecto a la exactitud, la sensibilidad, la especificidad y la precisión. El modelo final alcanza una exactitud del 97,44%, una especificidad del 97,76%, una sensibilidad del 96,97% y una precisión del 96,80%. Durante el desarrollo del modelo, el costo computacional y los recursos de memoria se tuvieron en cuenta para obtener un modelo de hardware eficiente en una futura  implementación del dispositivo