Evaluations of Thinning Algorithms for Preprocessing of Handwritten Characters

Thinning algorithms have played an important role in preprocessing phase which decides the success of recognition in the OCR system. This paper report on the performance of 11 thinning algorithms from the perspective of character recognition where different aspects of the performance of each algorithm like computing time, deviation from perfect 8-connectedness, and number of possible noise spurs present in the skeletons are considered

Feedback Based Architecture for Reading Check Courtesy Amounts

In recent years, a number of large-scale applications continue to rely heavily on the use of paper as the dominant medium, either on intra-organization basis or on inter-organization basis, including paper intensive applications in the check processing application. In many countries, the value of each check is read by human eyes before the check is physically transported, in stages, from the point it was presented to the location of the branch of the bank which issued the blank check to the concerned account holder. Such process of manual reading of each check involves significant time and cost. In this research, a new approach is introduced to read the numerical amount field on the check; also known as the courtesy amount field. In the case of check processing, the segmentation of unconstrained strings into individual digits is a challenging task because one needs to accommodate special cases involving: connected or overlapping digits, broken digits, and digits physically connected to a piece of stroke that belongs to a neighboring digit. The system described in this paper involves three stages: segmentation, normalization, and the recognition of each character using a neural network classifier, with results better than many other methods in the literaratu

Handwritten Bank Check Recognition of Courtesy Amounts

In spite of rapid evolution of electronic techniques, a number of large-scale applications continue to rely on the use of paper as the dominant medium. This is especially true for processing of bank checks. This paper examines the issue of reading the numerical amount field. In the case of checks, the segmentation of unconstrained strings into individual digits is a challenging task because of connected and overlapping digits, broken digits, and digits that are physically connected to pieces of strokes from neighboring digits. The proposed architecture involves four stages: segmentation of the string into individual digits, normalization, recognition of each character using a neural network classifier, and syntactic verification. Overall, this paper highlights the importance of employing a hybrid architecture that incorporates multiple approaches to provide high recognition rates

Description et classification des masses mammaires pour le diagnostic du cancer du sein

Le diagnostic assisté par ordinateur du cancer du sein devient de plus en plus une nécessité vu la croissance exponentielle du nombre de mammographies effectuées chaque année. En particulier, le diagnostic des masses mammaires et leur classification suscitent actuellement un grand intérêt. En effet, la complexité des formes traitées et la difficulté rencontrée afin de les discerner nécessitent l'usage de descripteurs appropriés. Dans ce travail, des méthodes de caractérisation adaptées aux pathologies mammaires sont proposées ainsi que l'étude de différentes méthodes de classification est abordée. Afin de pouvoir analyser les formes des masses, une étude concernant les différentes techniques de segmentation est réalisée. Cette étude nous a permis de nous orienter vers le modèle du level set basé sur la minimisation de l'énergie de la région évolutive. Une fois les images sont segmentées, une étude des différents descripteurs proposés dans la littérature est menée. Cependant, ces propositions présentent certaines limites telles que la sensibilité au bruit, la non invariance aux transformations géométriques et la description générale et imprécise des lésions. Dans ce contexte, nous proposons un nouveau descripteur intitulé les points terminaux du squelette (SEP) afin de caractériser les spiculations du contour des masses tout en respectant l'invariance à l'échelle. Un deuxième descripteur nommé la sélection des protubérances (PS) est proposé. Il assure de même le critère d'invariance et la description précise de la rugosité du contour. Toutefois, le SEP et le PS sont sensibles au bruit. Une troisième proposition intitulée le descripteur des masses spiculées (SMD) assurant une bonne robustesse au bruit est alors réalisée. Dans l'objectif de comparer différents descripteurs, une étude comparative entre différents classifieurs est effectuée. Les séparateurs à vaste marge (SVM) fournissent pour tous les descripteurs considérés le meilleur résultat de classification. Finalement, les descripteurs proposés ainsi que d'autres couramment utilisés dans le domaine du cancer du sein sont comparés afin de tester leur capacité à caractériser convenablement le contour des masses en question. La performance des trois descripteurs proposés et notamment le SMD est mise en évidence à travers les comparaisons effectuées.The computer-aided diagnosis of breast cancer is becoming increasingly a necessity given the exponential growth of performed mammograms. In particular, the breast mass diagnosis and classification arouse nowadays a great interest. Indeed, the complexity of processed forms and the difficulty to distinguish between them require the use of appropriate descriptors. In this work, characterization methods suitable for breast pathologies are proposed and the study of different classification methods is addressed. In order to analyze the mass shapes, a study about the different segmentation techniques in the context of breast mass detection is achieved. This study allows to adopt the level set model based on minimization of region-scalable fitting energy. Once the images are segmented, a study of various descriptors proposed inthe literature is conducted. Nevertheless, these proposals have some limitations such as sensitivity to noise, non invariance to geometric transformations and imprecise and general description of lesions. In this context, we propose a novel descriptor entitled the Skeleton End Points descriptor (SEP) in order to better characterize spiculations in mass contour while respecting the scale invariance. A second descriptor named the Protuberance Selection (PS) is proposed. It ensures also the same invariance criterion and the accurate description of the contour roughness. However, SEP and PS proposals are sensitive to noise. A third proposal entitled Spiculated Mass Descriptor (SMD) which has good robustness to noise is then carried out. In order to compare different descriptors, a comparative study between different classifiers is performed. The Support Vector Machine (SVM) provides for all considered descriptors the best classification result. Finally, the proposed descriptors and others commonly used in the breast cancer field are compared to test their ability to characterize the considered mass contours.EVRY-Bib. électronique (912289901) / SudocSudocFranceF