Arbitrary Keyword Spotting in Handwritten Documents

Despite the existence of electronic media in today’s world, a considerable amount of written communications is in paper form such as books, bank cheques, contracts, etc. There is an increasing demand for the automation of information extraction, classification, search, and retrieval of documents. The goal of this research is to develop a complete methodology for the spotting of arbitrary keywords in handwritten document images. We propose a top-down approach to the spotting of keywords in document images. Our approach is composed of two major steps: segmentation and decision. In the former, we generate the word hypotheses. In the latter, we decide whether a generated word hypothesis is a specific keyword or not. We carry out the decision step through a two-level classification where first, we assign an input image to a keyword or non-keyword class; and then transcribe the image if it is passed as a keyword. By reducing the problem from the image domain to the text domain, we do not only address the search problem in handwritten documents, but also the classification and retrieval, without the need for the transcription of the whole document image. The main contribution of this thesis is the development of a generalized minimum edit distance for handwritten words, and to prove that this distance is equivalent to an Ergodic Hidden Markov Model (EHMM). To the best of our knowledge, this work is the first to present an exact 2D model for the temporal information in handwriting while satisfying practical constraints. Some other contributions of this research include: 1) removal of page margins based on corner detection in projection profiles; 2) removal of noise patterns in handwritten images using expectation maximization and fuzzy inference systems; 3) extraction of text lines based on fast Fourier-based steerable filtering; 4) segmentation of characters based on skeletal graphs; and 5) merging of broken characters based on graph partitioning. Our experiments with a benchmark database of handwritten English documents and a real-world collection of handwritten French documents indicate that, even without any word/document-level training, our results are comparable with two state-of-the-art word spotting systems for English and French documents

Online Multi-Stage Deep Architectures for Feature Extraction and Object Recognition

Multi-stage visual architectures have recently found success in achieving high classification accuracies over image datasets with large variations in pose, lighting, and scale. Inspired by techniques currently at the forefront of deep learning, such architectures are typically composed of one or more layers of preprocessing, feature encoding, and pooling to extract features from raw images. Training these components traditionally relies on large sets of patches that are extracted from a potentially large image dataset. In this context, high-dimensional feature space representations are often helpful for obtaining the best classification performances and providing a higher degree of invariance to object transformations. Large datasets with high-dimensional features complicate the implementation of visual architectures in memory constrained environments. This dissertation constructs online learning replacements for the components within a multi-stage architecture and demonstrates that the proposed replacements (namely fuzzy competitive clustering, an incremental covariance estimator, and multi-layer neural network) can offer performance competitive with their offline batch counterparts while providing a reduced memory footprint. The online nature of this solution allows for the development of a method for adjusting parameters within the architecture via stochastic gradient descent. Testing over multiple datasets shows the potential benefits of this methodology when appropriate priors on the initial parameters are unknown. Alternatives to batch based decompositions for a whitening preprocessing stage which take advantage of natural image statistics and allow simple dictionary learners to work well in the problem domain are also explored. Expansions of the architecture using additional pooling statistics and multiple layers are presented and indicate that larger codebook sizes are not the only step forward to higher classification accuracies. Experimental results from these expansions further indicate the important role of sparsity and appropriate encodings within multi-stage visual feature extraction architectures

Word based off-line handwritten Arabic classification and recognition. Design of automatic recognition system for large vocabulary offline handwritten Arabic words using machine learning approaches.

The design of a machine which reads unconstrained words still remains an unsolved problem. For example, automatic interpretation of handwritten documents by a computer is still under research. Most systems attempt to segment words into letters and read words one character at a time. However, segmenting handwritten words is very difficult. So to avoid this words are treated as a whole. This research investigates a number of features computed from whole words for the recognition of handwritten words in particular. Arabic text classification and recognition is a complicated process compared to Latin and Chinese text recognition systems. This is due to the nature cursiveness of Arabic text. The work presented in this thesis is proposed for word based recognition of handwritten Arabic scripts. This work is divided into three main stages to provide a recognition system. The first stage is the pre-processing, which applies efficient pre-processing methods which are essential for automatic recognition of handwritten documents. In this stage, techniques for detecting baseline and segmenting words in handwritten Arabic text are presented. Then connected components are extracted, and distances between different components are analyzed. The statistical distribution of these distances is then obtained to determine an optimal threshold for word segmentation. The second stage is feature extraction. This stage makes use of the normalized images to extract features that are essential in recognizing the images. Various method of feature extraction are implemented and examined. The third and final stage is the classification. Various classifiers are used for classification such as K nearest neighbour classifier (k-NN), neural network classifier (NN), Hidden Markov models (HMMs), and the Dynamic Bayesian Network (DBN). To test this concept, the particular pattern recognition problem studied is the classification of 32492 words using ii the IFN/ENIT database. The results were promising and very encouraging in terms of improved baseline detection and word segmentation for further recognition. Moreover, several feature subsets were examined and a best recognition performance of 81.5% is achieved

Investigating the latency cost of statistical learning of a Gaussian mixture simulating on a convolutional density network with adaptive batch size technique for background modeling

Background modeling is a promising field of study in video analysis, with a wide range of applications in video surveillance. Deep neural networks have proliferated in recent years as a result of effective learning-based approaches to motion analysis. However, these strategies only provide a partial description of the observed scenes' insufficient properties since they use a single-valued mapping to estimate the target background's temporal conditional averages. On the other hand, statistical learning in the imagery domain has become one of the most widely used approaches due to its high adaptability to dynamic context transformation, especially Gaussian Mixture Models. Specifically, these probabilistic models aim to adjust latent parameters to gain high expectation of realistically observed data; however, this approach only concentrates on contextual dynamics in short-term analysis. In a prolonged investigation, it is challenging so that statistical methods cannot reserve the generalization of long-term variation of image data. Balancing the trade-off between traditional machine learning models and deep neural networks requires an integrated approach to ensure accuracy in conception while maintaining a high speed of execution. In this research, we present a novel two-stage approach for detecting changes using two convolutional neural networks in this work. The first architecture is based on unsupervised Gaussian mixtures statistical learning, which is used to classify the salient features of scenes. The second one implements a light-weighted pipeline of foreground detection. Our two-stage system has a total of approximately 3.5K parameters but still converges quickly to complex motion patterns. Our experiments on publicly accessible datasets demonstrate that our proposed networks are not only capable of generalizing regions of moving objects with promising results in unseen scenarios, but also competitive in terms of performance quality and effectiveness foreground segmentation. Apart from modeling the data's underlying generator as a non-convex optimization problem, we briefly examine the communication cost associated with the network training by using a distributed scheme of data-parallelism to simulate a stochastic gradient descent algorithm with communication avoidance for parallel machine learnin

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

Efficient Learning Machines

Computer scienc

Automatic interpretation of clock drawings for computerised assessment of dementia

The clock drawing test (CDT) is a standard neurological test for detection of cognitive impairment. A computerised version of the test has potential to improve test accessibility and accuracy. CDT sketch interpretation is one of the first stages in the analysis of the computerised test. It produces a set of recognised digits and symbols together with their positions on the clock face. Subsequently, these are used in the test scoring. This is a challenging problem because the average CDT taker has a high likelihood of cognitive impairment, and writing is one of the first functional activities to be affected. Current interpretation systems perform less well on this kind of data due to its unintelligibility. In this thesis, a novel automatic interpretation system for CDT sketch is proposed and developed. The proposed interpretation system and all the related algorithms developed in this thesis are evaluated using a CDT data set collected for this study. This data consist of two sets, the first set consisting of 65 drawings made by healthy people, and the second consisting of 100 drawings reproduced from drawings of dementia patients. This thesis has four main contributions. The first is a conceptual model of the proposed CDT sketch interpretation system based on integrating prior knowledge of the expected CDT sketch structure and human reasoning into the drawing interpretation system. The second is a novel CDT sketch segmentation algorithm based on supervised machine learning and a new set of temporal and spatial features automatically extracted from the CDT data. The evaluation of the proposed method shows that it outperforms the current state-of-the-art method for CDT drawing segmentation. The third contribution is a new v handwritten digit recognition algorithm based on a set of static and dynamic features extracted from handwritten data. The algorithm combines two classifiers, fuzzy k-nearest neighbour’s classifier with a Convolutional Neural Network (CNN), which take advantage both of static and dynamic data representation. The proposed digit recognition algorithm is shown to outperform each classifier individually in terms of recognition accuracy. The final contribution of this study is the probabilistic Situational Bayesian Network (SBN), which is a new hierarchical probabilistic model for addressing the problem of fusing diverse data sources, such as CDT sketches created by healthy volunteers and dementia patients, in a probabilistic Bayesian network. The evaluation of the proposed SBN-based CDT sketch interpretation system on CDT data shows highly promising results, with 100% recognition accuracy for heathy CDT drawings and 97.15% for dementia data. To conclude, the proposed automatic CDT sketch interpretation system shows high accuracy in terms of recognising different sketch objects and thus paves the way for further research in dementia and clinical computer-assisted diagnosis of dementia