Adaptive Algorithms for Automated Processing of Document Images

Large scale document digitization projects continue to motivate interesting document understanding technologies such as script and language identification, page classification, segmentation and enhancement. Typically, however, solutions are still limited to narrow domains or regular formats such as books, forms, articles or letters and operate best on clean documents scanned in a controlled environment. More general collections of heterogeneous documents challenge the basic assumptions of state-of-the-art technology regarding quality, script, content and layout. Our work explores the use of adaptive algorithms for the automated analysis of noisy and complex document collections. We first propose, implement and evaluate an adaptive clutter detection and removal technique for complex binary documents. Our distance transform based technique aims to remove irregular and independent unwanted foreground content while leaving text content untouched. The novelty of this approach is in its determination of best approximation to clutter-content boundary with text like structures. Second, we describe a page segmentation technique called Voronoi++ for complex layouts which builds upon the state-of-the-art method proposed by Kise [Kise1999]. Our approach does not assume structured text zones and is designed to handle multi-lingual text in both handwritten and printed form. Voronoi++ is a dynamically adaptive and contextually aware approach that considers components' separation features combined with Docstrum [O'Gorman1993] based angular and neighborhood features to form provisional zone hypotheses. These provisional zones are then verified based on the context built from local separation and high-level content features. Finally, our research proposes a generic model to segment and to recognize characters for any complex syllabic or non-syllabic script, using font-models. This concept is based on the fact that font files contain all the information necessary to render text and thus a model for how to decompose them. Instead of script-specific routines, this work is a step towards a generic character and recognition scheme for both Latin and non-Latin scripts

Advances in Manipulation and Recognition of Digital Ink

Handwriting is one of the most natural ways for a human to record knowledge. Recently, this type of human-computer interaction has received increasing attention due to the rapid evolution of touch-based hardware and software. While hardware support for digital ink reached its maturity, algorithms for recognition of handwriting in certain domains, including mathematics, are lacking robustness. Simultaneously, users may possess several pen-based devices and sharing of training data in adaptive recognition setting can be challenging. In addition, resolution of pen-based devices keeps improving making the ink cumbersome to process and store. This thesis develops several advances for efficient processing, storage and recognition of handwriting, which are applicable to the classification methods based on functional approximation. In particular, we propose improvements to classification of isolated characters and groups of rotated characters, as well as symbols of substantially different size. We then develop an algorithm for adaptive classification of handwritten mathematical characters of a user. The adaptive algorithm can be especially useful in the cloud-based recognition framework, which is described further in the thesis. We investigate whether the training data available in the cloud can be useful to a new writer during the training phase by extracting styles of individuals with similar handwriting and recommending styles to the writer. We also perform factorial analysis of the algorithm for recognition of n-grams of rotated characters. Finally, we show a fast method for compression of linear pieces of handwritten strokes and compare it with an enhanced version of the algorithm based on functional approximation of strokes. Experimental results demonstrate validity of the theoretical contributions, which form a solid foundation for the next generation handwriting recognition systems

Adaptive Methods for Robust Document Image Understanding

A vast amount of digital document material is continuously being produced as part of major digitization efforts around the world. In this context, generic and efficient automatic solutions for document image understanding represent a stringent necessity. We propose a generic framework for document image understanding systems, usable for practically any document types available in digital form. Following the introduced workflow, we shift our attention to each of the following processing stages in turn: quality assurance, image enhancement, color reduction and binarization, skew and orientation detection, page segmentation and logical layout analysis. We review the state of the art in each area, identify current defficiencies, point out promising directions and give specific guidelines for future investigation. We address some of the identified issues by means of novel algorithmic solutions putting special focus on generality, computational efficiency and the exploitation of all available sources of information. More specifically, we introduce the following original methods: a fully automatic detection of color reference targets in digitized material, accurate foreground extraction from color historical documents, font enhancement for hot metal typesetted prints, a theoretically optimal solution for the document binarization problem from both computational complexity- and threshold selection point of view, a layout-independent skew and orientation detection, a robust and versatile page segmentation method, a semi-automatic front page detection algorithm and a complete framework for article segmentation in periodical publications. The proposed methods are experimentally evaluated on large datasets consisting of real-life heterogeneous document scans. The obtained results show that a document understanding system combining these modules is able to robustly process a wide variety of documents with good overall accuracy

Past, present and future of historical information science

Der Bericht evaluiert Entwicklungen und Einflüsse von Forschungen im Bereich der empirisch orientierten Geschichtswissenschaft und deren rechnergestützten Methoden. Vorgestellt werden ein Forschungsparadigma und eine Forschungsinfrastruktur für die zukünftige historisch orientierte Informationswissenschaft. Die entscheidenden Anstöße dafür kommen eher von Außen, also nicht aus der scientific community der Assoziation for History and Computing (AHC). Die Gründe hierfür liegen darin, dass die AHC niemals klare Aussagen darüber gemacht hat, welches ihre Adressaten sind: Historiker, die sich für EDV interessieren, oder historisch orientierte Informationswissenschaftler. Das Ergebnis war, dass sich keine dieser Fraktionen angesprochen fühlte und kein Diskurs mit der 'traditionellen' Geschichtswissenschaft und der Informationswissenschaft zustande kam. Der Autor skizziert ein Forschungsprogramm, das diese Ambiguitäten vermeidet und die Ansätze in einer Forschungsinfrastruktur integriert. (ICAÜbers)'This report evaluates the impact of two decades of research within the framework of history and computing, and sets out a research paradigm and research infrastructure for future historical information science. It is good to see that there has been done a lot of historical information research in the past, much of it has been done, however, outside the field of history and computing, and not within a community like the Association for History and Computing. The reason is that the AHC never made a clear statement about what audience to address: historians with an interest in computing, or historical information scientists. As a result, both parties have not been accommodated, and communications with both 'traditional' history and 'information science' have not been established. A proper research program, based on new developments in information science, is proposed, along with an unambiguous scientific research infrastructure.' (author's abstract

COSPO/CENDI Industry Day Conference

The conference's objective was to provide a forum where government information managers and industry information technology experts could have an open exchange and discuss their respective needs and compare them to the available, or soon to be available, solutions. Technical summaries and points of contact are provided for the following sessions: secure products, protocols, and encryption; information providers; electronic document management and publishing; information indexing, discovery, and retrieval (IIDR); automated language translators; IIDR - natural language capabilities; IIDR - advanced technologies; IIDR - distributed heterogeneous and large database support; and communications - speed, bandwidth, and wireless

Neural Networks for Document Image and Text Processing

Nowadays, the main libraries and document archives are investing a considerable effort on digitizing their collections. Indeed, most of them are scanning the documents and publishing the resulting images without their corresponding transcriptions. This seriously limits the document exploitation possibilities. When the transcription is necessary, it is manually performed by human experts, which is a very expensive and error-prone task. Obtaining transcriptions to the level of required quality demands the intervention of human experts to review and correct the resulting output of the recognition engines. To this end, it is extremely useful to provide interactive tools to obtain and edit the transcription. Although text recognition is the final goal, several previous steps (known as preprocessing) are necessary in order to get a fine transcription from a digitized image. Document cleaning, enhancement, and binarization (if they are needed) are the first stages of the recognition pipeline. Historical Handwritten Documents, in addition, show several degradations, stains, ink-trough and other artifacts. Therefore, more sophisticated and elaborate methods are required when dealing with these kind of documents, even expert supervision in some cases is needed. Once images have been cleaned, main zones of the image have to be detected: those that contain text and other parts such as images, decorations, versal letters. Moreover, the relations among them and the final text have to be detected. Those preprocessing steps are critical for the final performance of the system since an error at this point will be propagated during the rest of the transcription process. The ultimate goal of the Document Image Analysis pipeline is to receive the transcription of the text (Optical Character Recognition and Handwritten Text Recognition). During this thesis we aimed to improve the main stages of the recognition pipeline, from the scanned documents as input to the final transcription. We focused our effort on applying Neural Networks and deep learning techniques directly on the document images to extract suitable features that will be used by the different tasks dealt during the following work: Image Cleaning and Enhancement (Document Image Binarization), Layout Extraction, Text Line Extraction, Text Line Normalization and finally decoding (or text line recognition). As one can see, the following work focuses on small improvements through the several Document Image Analysis stages, but also deals with some of the real challenges: historical manuscripts and documents without clear layouts or very degraded documents. Neural Networks are a central topic for the whole work collected in this document. Different convolutional models have been applied for document image cleaning and enhancement. Connectionist models have been used, as well, for text line extraction: first, for detecting interest points and combining them in text segments and, finally, extracting the lines by means of aggregation techniques; and second, for pixel labeling to extract the main body area of the text and then the limits of the lines. For text line preprocessing, i.e., to normalize the text lines before recognizing them, similar models have been used to detect the main body area and then to height-normalize the images giving more importance to the central area of the text. Finally, Convolutional Neural Networks and deep multilayer perceptrons have been combined with hidden Markov models to improve our transcription engine significantly. The suitability of all these approaches has been tested with different corpora for any of the stages dealt, giving competitive results for most of the methodologies presented.Hoy en día, las principales librerías y archivos está invirtiendo un esfuerzo considerable en la digitalización de sus colecciones. De hecho, la mayoría están escaneando estos documentos y publicando únicamente las imágenes sin transcripciones, limitando seriamente la posibilidad de explotar estos documentos. Cuando la transcripción es necesaria, esta se realiza normalmente por expertos de forma manual, lo cual es una tarea costosa y propensa a errores. Si se utilizan sistemas de reconocimiento automático se necesita la intervención de expertos humanos para revisar y corregir la salida de estos motores de reconocimiento. Por ello, es extremadamente útil para proporcionar herramientas interactivas con el fin de generar y corregir la transcripciones. Aunque el reconocimiento de texto es el objetivo final del Análisis de Documentos, varios pasos previos (preprocesamiento) son necesarios para conseguir una buena transcripción a partir de una imagen digitalizada. La limpieza, mejora y binarización de las imágenes son las primeras etapas del proceso de reconocimiento. Además, los manuscritos históricos tienen una mayor dificultad en el preprocesamiento, puesto que pueden mostrar varios tipos de degradaciones, manchas, tinta a través del papel y demás dificultades. Por lo tanto, este tipo de documentos requiere métodos de preprocesamiento más sofisticados. En algunos casos, incluso, se precisa de la supervisión de expertos para garantizar buenos resultados en esta etapa. Una vez que las imágenes han sido limpiadas, las diferentes zonas de la imagen deben de ser localizadas: texto, gráficos, dibujos, decoraciones, letras versales, etc. Por otra parte, también es importante conocer las relaciones entre estas entidades. Estas etapas del pre-procesamiento son críticas para el rendimiento final del sistema, ya que los errores cometidos en aquí se propagarán al resto del proceso de transcripción. El objetivo principal del trabajo presentado en este documento es mejorar las principales etapas del proceso de reconocimiento completo: desde las imágenes escaneadas hasta la transcripción final. Nuestros esfuerzos se centran en aplicar técnicas de Redes Neuronales (ANNs) y aprendizaje profundo directamente sobre las imágenes de los documentos, con la intención de extraer características adecuadas para las diferentes tareas: Limpieza y Mejora de Documentos, Extracción de Líneas, Normalización de Líneas de Texto y, finalmente, transcripción del texto. Como se puede apreciar, el trabajo se centra en pequeñas mejoras en diferentes etapas del Análisis y Procesamiento de Documentos, pero también trata de abordar tareas más complejas: manuscritos históricos, o documentos que presentan degradaciones. Las ANNs y el aprendizaje profundo son uno de los temas centrales de esta tesis. Diferentes modelos neuronales convolucionales se han desarrollado para la limpieza y mejora de imágenes de documentos. También se han utilizado modelos conexionistas para la extracción de líneas: primero, para detectar puntos de interés y segmentos de texto y, agregarlos para extraer las líneas del documento; y en segundo lugar, etiquetando directamente los píxeles de la imagen para extraer la zona central del texto y así definir los límites de las líneas. Para el preproceso de las líneas de texto, es decir, la normalización del texto antes del reconocimiento final, se han utilizado modelos similares a los mencionados para detectar la zona central del texto. Las imagenes se rescalan a una altura fija dando más importancia a esta zona central. Por último, en cuanto a reconocimiento de escritura manuscrita, se han combinado técnicas de ANNs y aprendizaje profundo con Modelos Ocultos de Markov, mejorando significativamente los resultados obtenidos previamente por nuestro motor de reconocimiento. La idoneidad de todos estos enfoques han sido testeados con diferentes corpus en cada una de las tareas tratadas., obtenieAvui en dia, les principals llibreries i arxius històrics estan invertint un esforç considerable en la digitalització de les seues col·leccions de documents. De fet, la majoria estan escanejant aquests documents i publicant únicament les imatges sense les seues transcripcions, fet que limita seriosament la possibilitat d'explotació d'aquests documents. Quan la transcripció del text és necessària, normalment aquesta és realitzada per experts de forma manual, la qual cosa és una tasca costosa i pot provocar errors. Si s'utilitzen sistemes de reconeixement automàtic es necessita la intervenció d'experts humans per a revisar i corregir l'eixida d'aquests motors de reconeixement. Per aquest motiu, és extremadament útil proporcionar eines interactives amb la finalitat de generar i corregir les transcripcions generades pels motors de reconeixement. Tot i que el reconeixement del text és l'objectiu final de l'Anàlisi de Documents, diversos passos previs (coneguts com preprocessament) són necessaris per a l'obtenció de transcripcions acurades a partir d'imatges digitalitzades. La neteja, millora i binarització de les imatges (si calen) són les primeres etapes prèvies al reconeixement. A més a més, els manuscrits històrics presenten una major dificultat d'analisi i preprocessament, perquè poden mostrar diversos tipus de degradacions, taques, tinta a través del paper i altres peculiaritats. Per tant, aquest tipus de documents requereixen mètodes de preprocessament més sofisticats. En alguns casos, fins i tot, es precisa de la supervisió d'experts per a garantir bons resultats en aquesta etapa. Una vegada que les imatges han sigut netejades, les diferents zones de la imatge han de ser localitzades: text, gràfics, dibuixos, decoracions, versals, etc. D'altra banda, també és important conéixer les relacions entre aquestes entitats i el text que contenen. Aquestes etapes del preprocessament són crítiques per al rendiment final del sistema, ja que els errors comesos en aquest moment es propagaran a la resta del procés de transcripció. L'objectiu principal del treball que estem presentant és millorar les principals etapes del procés de reconeixement, és a dir, des de les imatges escanejades fins a l'obtenció final de la transcripció del text. Els nostres esforços se centren en aplicar tècniques de Xarxes Neuronals (ANNs) i aprenentatge profund directament sobre les imatges de documents, amb la intenció d'extraure característiques adequades per a les diferents tasques analitzades: neteja i millora de documents, extracció de línies, normalització de línies de text i, finalment, transcripció. Com es pot apreciar, el treball realitzat aplica xicotetes millores en diferents etapes de l'Anàlisi de Documents, però també tracta d'abordar tasques més complexes: manuscrits històrics, o documents que presenten degradacions. Les ANNs i l'aprenentatge profund són un dels temes centrals d'aquesta tesi. Diferents models neuronals convolucionals s'han desenvolupat per a la neteja i millora de les dels documents. També s'han utilitzat models connexionistes per a la tasca d'extracció de línies: primer, per a detectar punts d'interés i segments de text i, agregar-los per a extraure les línies del document; i en segon lloc, etiquetant directament els pixels de la imatge per a extraure la zona central del text i així definir els límits de les línies. Per al preprocés de les línies de text, és a dir, la normalització del text abans del reconeixement final, s'han utilitzat models similars als utilitzats per a l'extracció de línies. Finalment, quant al reconeixement d'escriptura manuscrita, s'han combinat tècniques de ANNs i aprenentatge profund amb Models Ocults de Markov, que han millorat significativament els resultats obtinguts prèviament pel nostre motor de reconeixement. La idoneïtat de tots aquests enfocaments han sigut testejats amb diferents corpus en cadascuna de les tasques tractadPastor Pellicer, J. (2017). Neural Networks for Document Image and Text Processing [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/90443TESI

Sentiment Analysis for Social Media

Sentiment analysis is a branch of natural language processing concerned with the study of the intensity of the emotions expressed in a piece of text. The automated analysis of the multitude of messages delivered through social media is one of the hottest research fields, both in academy and in industry, due to its extremely high potential applicability in many different domains. This Special Issue describes both technological contributions to the field, mostly based on deep learning techniques, and specific applications in areas like health insurance, gender classification, recommender systems, and cyber aggression detection

Graphonomics and your Brain on Art, Creativity and Innovation : Proceedings of the 19th International Graphonomics Conference (IGS 2019 – Your Brain on Art)

[Italiano]: “Grafonomia e cervello su arte, creatività e innovazione”. Un forum internazionale per discutere sui recenti progressi nell'interazione tra arti creative, neuroscienze, ingegneria, comunicazione, tecnologia, industria, istruzione, design, applicazioni forensi e mediche. I contributi hanno esaminato lo stato dell'arte, identificando sfide e opportunità, e hanno delineato le possibili linee di sviluppo di questo settore di ricerca. I temi affrontati includono: strategie integrate per la comprensione dei sistemi neurali, affettivi e cognitivi in ambienti realistici e complessi; individualità e differenziazione dal punto di vista neurale e comportamentale; neuroaesthetics (uso delle neuroscienze per spiegare e comprendere le esperienze estetiche a livello neurologico); creatività e innovazione; neuro-ingegneria e arte ispirata dal cervello, creatività e uso di dispositivi di mobile brain-body imaging (MoBI) indossabili; terapia basata su arte creativa; apprendimento informale; formazione; applicazioni forensi. / [English]: “Graphonomics and your brain on art, creativity and innovation”. A single track, international forum for discussion on recent advances at the intersection of the creative arts, neuroscience, engineering, media, technology, industry, education, design, forensics, and medicine. The contributions reviewed the state of the art, identified challenges and opportunities and created a roadmap for the field of graphonomics and your brain on art. The topics addressed include: integrative strategies for understanding neural, affective and cognitive systems in realistic, complex environments; neural and behavioral individuality and variation; neuroaesthetics (the use of neuroscience to explain and understand the aesthetic experiences at the neurological level); creativity and innovation; neuroengineering and brain-inspired art, creative concepts and wearable mobile brain-body imaging (MoBI) designs; creative art therapy; informal learning; education; forensics

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