Information Preserving Processing of Noisy Handwritten Document Images

Many pre-processing techniques that normalize artifacts and clean noise induce anomalies due to discretization of the document image. Important information that could be used at later stages may be lost. A proposed composite-model framework takes into account pre-printed information, user-added data, and digitization characteristics. Its benefits are demonstrated by experiments with statistically significant results. Separating pre-printed ruling lines from user-added handwriting shows how ruling lines impact people\u27s handwriting and how they can be exploited for identifying writers. Ruling line detection based on multi-line linear regression reduces the mean error of counting them from 0.10 to 0.03, 6.70 to 0.06, and 0.13 to 0.02, com- pared to an HMM-based approach on three standard test datasets, thereby reducing human correction time by 50%, 83%, and 72% on average. On 61 page images from 16 rule-form templates, the precision and recall of form cell recognition are increased by 2.7% and 3.7%, compared to a cross-matrix approach. Compensating for and exploiting ruling lines during feature extraction rather than pre-processing raises the writer identification accuracy from 61.2% to 67.7% on a 61-writer noisy Arabic dataset. Similarly, counteracting page-wise skew by subtracting it or transforming contours in a continuous coordinate system during feature extraction improves the writer identification accuracy. An implementation study of contour-hinge features reveals that utilizing the full probabilistic probability distribution function matrix improves the writer identification accuracy from 74.9% to 79.5%

Digitizing pathology lab workflows using image processing and OCR

The pathology lab at Haukeland University Hospital is currently facing a few challenges. In the lab they process different specimens of tissue samples for various reasons such as looking for signs of cancers and tumours. The last six years the lab has had an average increase of 3.42% in the amount of test samples which needs to be analysed each year. This increase has led to queues forming at various stages of sample analysis. Specific samples are hard to locate within these queues and the queues lead to slowdowns of sample processing. The pathology lab require a better solution to the way and order in which samples are processed and tracked, to do so they need to gather data about the processing by implementing a tracking solution. This project aims to help them achieve this goal by looking for a potential software solution to part of the problem. This solution aims to take advantage of technologies such as optical character recognition (OCR) for detecting and tracking samples. The goal for this research is to create and test solutions for cassette detection and identification using pre-trained image processing libraries. Testing two different methods, these being edge detection and the EAST neural network, they achieved an accuracy of 77.84% and 93.41% respectively regarding cassette detection. Tesseract OCR performance of detected cassettes also varies between the two methods, giving an accuracy score of 36.1% when using edge detection and 62.1% using EAST. The increase in accuracy comes at a cost in runtime. In addition to these evaluations an in-lab trial compares the sorting time for the current solution of manual sorting versus the efficiency of sorting using the proposed digital solution. The trial concluded that the proposed digital solution is able to increase the amount of cassettes sorted within a set amount of time by 54% decreasing the time spent on manual sorting activities by 35%. This thesis also covers some of the interaction design decisions for the proposed application to allow for manual error correction. Through conceptual designs the thesis shows how the proposed system could interface with a process execution engine. There is also a proposal for what the architecture of an integrated system could look like. Integrating this system would allow for the generation of fine-grained event logs for process mining purposes. The data from these logs have a possibility of leading to future improvements in pathology lab workflow.Masteroppgåve i informasjonsvitskapINFO390MASV-INF

Feature-based methodology for supporting architecture refactoring and maintenance of long-life software systems

Zusammenfassung Langlebige Software-Systeme durchlaufen viele bedeutende Veraenderungen im Laufe ihres Lebenszyklus, um der Weiterentwicklung der Problemdomaenen zu folgen. Normalerweise ist es schwierig eine Software-Systemarchitektur den schnellen Weiterentwicklungen einer Problemdomaene anzupassen und mit der Zeit wird der Unterschied zwischen der Problemdomaene und der Software-Systemarchitektur zu groß, um weitere Softwareentwicklung sinnvoll fortzufuehren. Fristgerechte Refactorings der Systemarchitektur sind notwendig, um dieses Problem zu vermeiden. Aufgrund des verhaeltnismaeßig hohen Gefahrenpotenzials und des zeitlich stark verzoegerten Nutzens von Refactorings, werden diese Maßnahmen normalerweise bis zum letztmoeglichen Zeitpunkt hinausgeschoben. In der Regel ist das Management abgeneigt Architektur-Refactorings zu akzeptieren, außer diese sind absolut notwendig. Die bevorzugte Vorgehensweise ist, neue Systemmerkmale ad hoc hinzuzufuegen und nach dem Motto ”Aendere nie etwas an einem funktionierenden System!” vorzugehen. Letztlich ist das Ergebnis ein Architekturzerfall (Architekturdrift). Die Notwendigkeit kleiner Refactoring-Schritte fuehrt zur Notwendigkeit des Architektur-Reengineerings. Im Gegensatz zum Refactoring, das eine normale Entwicklungstaetigkeit darstellt, ist Reengineering eine Form der Software- ”Revolution”. Reengineeringprojekte sind sehr riskant und kostspielig. Der Nutzen des Reengineerings ist normalerweise nicht so hoch wie erwartet. Wenn nach dem Reengineering schließlich die erforderlichen Architekturaenderungen statt.nden, kann dies zu spaet sein. Trotz der enormen in das Projekt gesteckten Bemuehungen erfuellen die Resultate des Reengineerings normalerweise nicht die Erwartungen. Es kann passieren, dass sehr bald ein neues, kostspieliges Reengineering erforderlich wird. In dieser Arbeit werden das Problem der Softwareevolution und der Zerfall von Softwarearchitekturen behandelt. Eine Methode wird vorgestellt, welche die Softwareentwicklung in ihrer entscheidenden Phase, dem Architekturrefactoring, unterstuetzt. Die Softwareentwicklung wird sowohl in technischer als auch organisatorischer Hinsicht unterstuetzt. Diese Arbeit hat neue Techniken entwickelt, welche die Reverse-Engineering-, Architecture-Recovery- und Architecture-Redesign-Taetigkeiten unterst uetzen. Sie schlaegt auch Aenderungen des Softwareentwicklungsprozesses vor, die fristgerechte Architekturrefactorings erzwingen koennen und damit die Notwendigkeit der Durchfuehrung eines Architektur- Reengineerings vermeiden. In dieser Arbeit wird die Merkmalmodellierung als Hauptinstrument verwendet. Merkmale werden genutzt, um die Abstraktionsluecke zwischen den Anforderungen der Problemdomaene und der Systemarchitektur zu fuellen. Merkmalmodelle werden auch als erster Grundriss fr die Wiederherstellung der verlorenen Systemarchitektur genutzt. Merkmalbasierte Analysen fuehren zu diversen, nuetzlichen Hinweisen fuer den erneuten Entwurf (das Re-Design) einer Architektur. Schließlich wird die Merkmalmodellierung als Kommunikationsmittel zwischen unterschiedlichen Projektbeteiligten (Stakeholdern) im Verlauf des Softwareengineering-Prozesses verwendet und auf dieser Grundlage wird ein neuer Anforderungsde.nitionsprozess vorgeschlagen, der die erforderlichen Architekturrefactorings erzwingt.The long-life software systems withstand many significant changes throughout their life-cycle in order to follow the evolution of the problem domains. Usually, the software system architecture can not follow the rapid evolution of a problem domain and with time, the diversion of the architecture in respect to the domain features becomes prohibiting for software evolution. For avoiding this problem, periodical refactorings of the system architecture are required. Usually, architecture refactorings are postponed until the very last moment, because of the relatively high risk involved and the lack of short-term profit. As a rule, the management is unwilling to accept architecture refactorings unless they become absolutely necessary. The preferred way of working is to add new system features in an ad-hoc manner and to keep the rule ”Never touch a running system!”. The final result is an architecture decay. The need of performing small refactoring activities turns into need for architecture reengineering. In contrast to refactoring, which is a normal evolutionary activity, reengineering is a kind of software ”revolution”. Reengineering projects are risky and expensive. The effectiveness of reengineering is also usually not as high as expected. When finally after reengineering the required architecture changes take place, it can be too late. Despite the enormous invested efforts, the results of the reengineering usually do not satisfy the expectations. It might happen that very soon a new expensive reengineering is required. This thesis deals with the problem of software evolution and the decay of software architectures. It presents a method, which assists software evolution in its crucial part, the architecture refactoring. The assistance is performed for both technical and organizational aspects of the software evolution. The thesis provides new techniques for supporting reverse engineering, architecture recovery and redesigning activities. It also proposes changes to the software engineering process, which can force timely architecture refactorings and thus avoid the need of performing architecture reengineering. For the work in this thesis feature modeling is utilized as a main asset. Features are used to fill the abstraction gap between domain requirements and system architecture. Feature models are also used as an outline for recovering of lost system architectures. Through feature-based analyses a number of useful hints and clues for architecture redesign are produced. Finally, feature modeling is used as a communication between different stakeholders of the software engineering process and on this basis a new requirements engineering process is proposed, which forces the needed architecture refactorings

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

Contributions au tri automatique de documents et de courrier d'entreprises

Ce travail de thèse s inscrit dans le cadre du développement de systèmes de vision industrielle pour le tri automatique de documents et de courriers d entreprises. Les architectures existantes, dont nous avons balayé les spécificités dans les trois premiers chapitres de la thèse, présentent des faiblesses qui se traduisent par des erreurs de lecture et des rejets que l on impute encore trop souvent aux OCR. Or, les étapes responsables de ces rejets et de ces erreurs de lecture sont les premières à intervenir dans le processus. Nous avons ainsi choisi de porter notre contribution sur les aspects inhérents à la segmentation des images de courriers et la localisation de leurs régions d intérêt en investissant une nouvelle approche pyramidale de modélisation par coloration hiérarchique de graphes ; à ce jour, la coloration de graphes n a jamais été exploitée dans un tel contexte. Elle intervient dans notre contribution à toutes les étapes d analyse de la structure des documents ainsi que dans la prise de décision pour la reconnaissance (reconnaissance de la nature du document à traiter et reconnaissance du bloc adresse). Notre architecture a été conçue pour réaliser essentiellement les étapes d analyse de structures et de reconnaissance en garantissant une réelle coopération entres les différents modules d analyse et de décision. Elle s articule autour de trois grandes parties : une partie de segmentation bas niveau (binarisation et recherche de connexités), une partie d extraction de la structure physique par coloration hiérarchique de graphe et une partie de localisation de blocs adresse et de classification de documents. Les algorithmes impliqués dans le système ont été conçus pour leur rapidité d exécution (en adéquation avec les contraintes de temps réels), leur robustesse, et leur compatibilité. Les expérimentations réalisées dans ce contexte sont très encourageantes et offrent également de nouvelles perspectives à une plus grande diversité d images de documents.This thesis deals with the development of industrial vision systems for automatic business documents and mail sorting. These systems need very high processing time, accuracy and precision of results. The current systems are most of time made of sequential modules needing fast and efficient algorithms throughout the processing line: from low to high level stages of analysis and content recognition. The existing architectures that we have described in the three first chapters of the thesis have shown their weaknesses that are expressed by reading errors and OCR rejections. The modules that are responsible of these rejections and reading errors are mostly the first to occur in the processes of image segmentation and interest regions location. Indeed, theses two processes, involving each other, are fundamental for the system performances and the efficiency of the automatic sorting lines. In this thesis, we have chosen to focus on different sides of mail images segmentation and of relevant zones (as address block) location. We have chosen to develop a model based on a new pyramidal approach using a hierarchical graph coloring. As for now, graph coloring has never been exploited in such context. It has been introduced in our contribution at every stage of document layout analysis for the recognition and decision tasks (kind of document or address block recognition). The recognition stage is made about a training process with a unique model of graph b-coloring. Our architecture is basically designed to guarantee a good cooperation bewtween the different modules of decision and analysis for the layout analysis and the recognition stages. It is composed of three main sections: the low-level segmentation (binarisation and connected component labeling), the physical layout extraction by hierarchical graph coloring and the address block location and document sorting. The algorithms involved in the system have been designed for their execution speed (matching with real time constraints), their robustness, and their compatibility. The experimentations made in this context are very encouraging and lead to investigate a wider diversity of document images.VILLEURBANNE-DOC'INSA-Bib. elec. (692669901) / SudocSudocFranceF

Document preprocessing and fuzzy unsupervised character classification

This dissertation presents document preprocessing and fuzzy unsupervised character classification for automatically reading daily-received office documents that have complex layout structures, such as multiple columns and mixed-mode contents of texts, graphics and half-tone pictures. First, the block segmentation algorithm is performed based on a simple two-step run-length smoothing to decompose a document into single-mode blocks. Next, the block classification is performed based on the clustering rules to classify each block into one of the types such as text, horizontal or vertical lines, graphics, and pictures. The mean white-to-black transition is shown as an invariance for textual blocks, and is useful for block discrimination. A fuzzy model for unsupervised character classification is designed to improve the robustness, correctness, and speed of the character recognition system. The classification procedures are divided into two stages. The first stage separates the characters into seven typographical categories based on word structures of a text line. The second stage uses pattern matching to classify the characters in each category into a set of fuzzy prototypes based on a nonlinear weighted similarity function. A fuzzy model of unsupervised character classification, which is more natural in the representation of prototypes for character matching, is defined and the weighted fuzzy similarity measure is explored. The characteristics of the fuzzy model are discussed and used in speeding up the classification process. After classification, the character recognition procedure is simply applied on the limited versions of the fuzzy prototypes. To avoid information loss and extra distortion, an topography-based approach is proposed to apply directly on the fuzzy prototypes to extract the skeletons. First, a convolution by a bell-shaped function is performed to obtain a smooth surface. Second, the ridge points are extracted by rule-based topographic analysis of the structure. Third, a membership function is assigned to ridge points with values indicating the degrees of membership with respect to the skeleton of an object. Finally, the significant ridge points are linked to form strokes of skeleton, and the clues of eigenvalue variation are used to deal with degradation and preserve connectivity. Experimental results show that our algorithm can reduce the deformation of junction points and correctly extract the whole skeleton although a character is broken into pieces. For some characters merged together, the breaking candidates can be easily located by searching for the saddle points. A pruning algorithm is then applied on each breaking position. At last, a multiple context confirmation can be applied to increase the reliability of breaking hypotheses

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Recognition of Japanese handwritten characters with Machine learning techniques

The recognition of Japanese handwritten characters has always been a challenge for researchers. A large number of classes, their graphic complexity, and the existence of three different writing systems make this problem particularly difficult compared to Western writing. For decades, attempts have been made to address the problem using traditional OCR (Optical Character Recognition) techniques, with mixed results. With the recent popularization of machine learning techniques through neural networks, this research has been revitalized, bringing new approaches to the problem. These new results achieve performance levels comparable to human recognition. Furthermore, these new techniques have allowed collaboration with very different disciplines, such as the Humanities or East Asian studies, achieving advances in them that would not have been possible without this interdisciplinary work. In this thesis, these techniques are explored until reaching a sufficient level of understanding that allows us to carry out our own experiments, training neural network models with public datasets of Japanese characters. However, the scarcity of public datasets makes the task of researchers remarkably difficult. Our proposal to minimize this problem is the development of a web application that allows researchers to easily collect samples of Japanese characters through the collaboration of any user. Once the application is fully operational, the examples collected until that point will be used to create a new dataset in a specific format. Finally, we can use the new data to carry out comparative experiments with the previous neural network models