Improvement and expansion of a decoder module for an OCR system

OCR systems, short for Optical Character Recognition, are becoming increasingly popular due to the increase in the digitalization of everything. Books, textbooks, magazines and several other paper-based documents are being transformed into an electronic version to be manipulated by a computer. As well, instant translation by image is becoming a reality with the booming technology of smartphones. Nonetheless, OCR systems are still not perfect. The real world contains a lot of extra information and noise that is very difficult for a current OCR system to clean completely, as well as the immensity of variables that take place in handwritten characters and paper-based documents. This project is meant to further improve a decoding module that uses a graph-based algorithm to predict optimal words, and attempts to increase its overall accuracy by using synthetic dataset generation for testing and applying improvements to the base algorithm.Els sistemes OCR, de l'anglès Optical Character Recognition, s'estàn popularitzant considerablement degut a l'augment en la digitalització del món. Llibres de lectura, llibres de text, revistes i altres documents impresos s'estàn transformant en versions digitals per a ser manipulades a través d'ordinadors. A més a més, la traducció instantània a través d'imatge s'està convertint en una realitat amb la tecnologia dels mòbils intel·ligents. No obstant, els sistemes OCR encara no són perfectes. El món real conté molta informació adicional i soroll que són molt complicats d'eliminar per a un sistema OCR actual, a més a més de la immensa quantitat de variables que trobem als caràcters manuscrits i als documents a paper. Aquest projecte millora un mòdul decodificador que fa servir un algorisme basat en grafs per a predir paraules òptimes, i millora els seus resultats utilitzant conjunts de dades generats sintèticament i aplicant modificacions per a millorar l'algorisme base.Los sistemas OCR, del inglés Optical Character Recognition, se están popularizando considerablemente debido al aumento en la digitalización del mundo. Libros de lectura, libros de texto, revistas y otros documentos impresos se están transformando en versiones digitales para ser manipuladas a través de ordenadores. Además, la traducción instantánea a través de imagen se está convirtiendo en una realidad con la tecnología de los móviles inteligentes. No obstante, los sistemas OCR aún no son perfectos. El mundo real contiene mucha información adicional y ruido que son muy complicados de eliminar para un sistema OCR actual, además de la inmensa cantidad de variables que encontramos en los caracteres manuscritos y los documentos a papel. Este proyecto mejora un módulo decodificador que utiliza un algoritmo basado en grafos para predecir palabras óptimas, y mejora sus resultados utilizando conjuntos de datos generados sintéticamente y aplicando modificaciones para mejorar el algoritmo base

Supporting Multi-Domain Model Management

Model-driven engineering has been used in different domains such as software engineering, robotics, and automotive. This approach has models as the primary artifacts, and it is expected to improve quality of system specification and design, as well as the communication among the development team. Managing models that belong to the same domain might not be a complex task because of the features provided by the available development tools. However, managing interrelated models of different domains is challenging. A robot is an example of such a multi-domain system. To develop it one might need to combine models created by experts from mechanics, electronics and software domains. These models might be created using domain specific tools of each domain, and a change in one model of one domain might impact a model from a different domain causing inconsistency in the entire system. This thesis therefore aims to facilitate the evolution of the models in this multi-domain setting. It starts with a systematic literature review in order to identify the open issues, and strategies used to manage models from different domains. We identified that making explicit the relationship between models from different domains can support the models maintenance, making it easy to recognize affected models because of a change. The following step was to investigate ways of extracting information from different engineering models that were created using different modeling notations. For this goal, we required a uniform approach that would be independent from the peculiarities of the notations. This uniform approach can only be based on elements typically present in various modeling notations, i.e., text, boxes, and lines. Thus, we investigated the suitability of optical character recognition (OCR) for extracting textual elements from models from different domains. We also identified the common errors made by the off-the-shelf OCR services, and we proposed two approaches to correct one of these errors. After that, we used name matching techniques on the textual elements extracted by OCR to identify relationships between models from different domains. To conclude, we created an infrastructure that combines all the previous elements into one single tool that can also store the relationships in a structured manner making it easier to maintain the consistency of an entire system. We evaluated it by means of an observational study with a multidisciplinary team that builds autonomous robots designed to play football