Decision Fusion and Contextual Information for Arabic Words Recognition for Computing and Informatics

The study of multiple classifier systems has become recently an area of intensive research in pattern recognition. Also in handwriting recognition, systems combining several classifiers have been investigated. An approach for recognizing the legal amount for handwritten Arabic bank check is described in this article. The solution uses multiple information sources to recognize words. The recognition step is preformed with a parallel combination of three kinds of classifiers using holistic word structural features. The classification stage results are first normalized, and the sum combination is performed as a decision fusion scheme, after which a syntactic analyzer makes final decision on the candidate words. Using this approach, the obtained results are very interesting and promising

Classification of Graphomotor Impressions Using Convolutional Neural Networks: An Application to Automated Neuro-Psychological Screening Tests

Graphomotor impressions are a product of complex cognitive, perceptual and motor skills and are widely used as psychometric tools for the diagnosis of a variety of neuro-psychological disorders. Apparent deformations in these responses are quantified as errors and are used are indicators of various conditions. Contrary to conventional assessment methods where manual analysis of impressions is carried out by trained clinicians, an automated scoring system is marked by several challenges. Prior to analysis, such computerized systems need to extract and recognize individual shapes drawn by subjects on a sheet of paper as an important pre-processing step. The aim of this study is to apply deep learning methods to recognize visual structures of interest produced by subjects. Experiments on figures of Bender Gestalt Test (BGT), a screening test for visuo-spatial and visuo-constructive disorders, produced by 120 subjects, demonstrate that deep feature representation brings significant improvements over classical approaches. The study is intended to be extended to discriminate coherent visual structures between produced figures and expected prototypes

Character Recognition

Character recognition is one of the pattern recognition technologies that are most widely used in practical applications. This book presents recent advances that are relevant to character recognition, from technical topics such as image processing, feature extraction or classification, to new applications including human-computer interfaces. The goal of this book is to provide a reference source for academic research and for professionals working in the character recognition field

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

Off-line Thai handwriting recognition in legal amount

Thai handwriting in legal amounts is a challenging problem and a new field in the area of handwriting recognition research. The focus of this thesis is to implement Thai handwriting recognition system. A preliminary data set of Thai handwriting in legal amounts is designed. The samples in the data set are characters and words of the Thai legal amounts and a set of legal amounts phrases collected from a number of native Thai volunteers. At the preprocessing and recognition process, techniques are introduced to improve the characters recognition rates. The characters are divided into two smaller subgroups by their writing levels named body and high groups. The recognition rates of both groups are increased based on their distinguished features. The writing level separation algorithms are implemented using the size and position of characters. Empirical experiments are set to test the best combination of the feature to increase the recognition rates. Traditional recognition systems are modified to give the accumulative top-3 ranked answers to cover the possible character classes. At the postprocessing process level, the lexicon matching algorithms are implemented to match the ranked characters with the legal amount words. These matched words are joined together to form possible choices of amounts. These amounts will have their syntax checked in the last stage. Several syntax violations are caused by consequence faulty character segmentation and recognition resulting from connecting or broken characters. The anomaly in handwriting caused by these characters are mainly detected by their size and shape. During the recovery process, the possible word boundary patterns can be pre-defined and used to segment the hypothesis words. These words are identified by the word recognition and the results are joined with previously matched words to form the full amounts and checked by the syntax rules again. From 154 amounts written by 10 writers, the rejection rate is 14.9 percent with the recovery processes. The recognition rate for the accepted amount is 100 percent

Large vocabulary recognition for online Turkish handwriting with sublexical units

We present a system for large vocabulary recognition of online Turkish handwriting, using hidden Markov models. While using a traditional approach for the recognizer, we have identified and developed solutions for the main problems specific to Turkish handwriting recognition. First, since large amounts of Turkish handwriting samples are not available, the system is trained and optimized using the large UNIPEN dataset of English handwriting, before extending it to Turkish using a small Turkish dataset. The delayed strokes, which pose a significant source of variation in writing order due to the large number of diacritical marks in Turkish, are removed during preprocessing. Finally, as a solution to the high out-of-vocabulary rates encountered when using a fixed size lexicon in general purpose recognition, a lexicon is constructed from sublexical units (stems and endings) learned from a large Turkish corpus. A statistical bigram language model learned from the same corpus is also applied during the decoding process. The system obtains a 91.7% word recognition rate when tested on a small Turkish handwritten word dataset using a medium sized (1950 words) lexicon corresponding to the vocabulary of the test set and 63.8% using a large, general purpose lexicon (130,000 words). However, with the proposed stem+ending lexicon (12,500 words) and bigram language model with lattice expansion, a 67.9% word recognition accuracy is obtained, surpassing the results obtained with the general purpose lexicon while using a much smaller one

Feature Extraction Methods for Character Recognition

Not Include