266 research outputs found

    Multimodal Interactive Transcription of Handwritten Text Images

    Full text link
    En esta tesis se presenta un nuevo marco interactivo y multimodal para la transcripción de Documentos manuscritos. Esta aproximación, lejos de proporcionar la transcripción completa pretende asistir al experto en la dura tarea de transcribir. Hasta la fecha, los sistemas de reconocimiento de texto manuscrito disponibles no proporcionan transcripciones aceptables por los usuarios y, generalmente, se requiere la intervención del humano para corregir las transcripciones obtenidas. Estos sistemas han demostrado ser realmente útiles en aplicaciones restringidas y con vocabularios limitados (como es el caso del reconocimiento de direcciones postales o de cantidades numéricas en cheques bancarios), consiguiendo en este tipo de tareas resultados aceptables. Sin embargo, cuando se trabaja con documentos manuscritos sin ningún tipo de restricción (como documentos manuscritos antiguos o texto espontáneo), la tecnología actual solo consigue resultados inaceptables. El escenario interactivo estudiado en esta tesis permite una solución más efectiva. En este escenario, el sistema de reconocimiento y el usuario cooperan para generar la transcripción final de la imagen de texto. El sistema utiliza la imagen de texto y una parte de la transcripción previamente validada (prefijo) para proponer una posible continuación. Despues, el usuario encuentra y corrige el siguente error producido por el sistema, generando así un nuevo prefijo mas largo. Este nuevo prefijo, es utilizado por el sistema para sugerir una nueva hipótesis. La tecnología utilizada se basa en modelos ocultos de Markov y n-gramas. Estos modelos son utilizados aquí de la misma manera que en el reconocimiento automático del habla. Algunas modificaciones en la definición convencional de los n-gramas han sido necesarias para tener en cuenta la retroalimentación del usuario en este sistema.Romero Gómez, V. (2010). Multimodal Interactive Transcription of Handwritten Text Images [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/8541Palanci

    2D Grammar Extension of the CMP Mathematical Formulae On-line Recognition System

    Get PDF
    Projecte realitzat en col.laboració amb Czech Technical University in PragueIn the last years, the recognition of handwritten mathematical formulae has recieved an increasing amount of attention in pattern recognition research. However, the diversity of approaches to the problem and the lack of a commercially viable system indicate that there is still much research to be done in this area. In this thesis, I will describe the previous work on a system for on-line handwritten mathematical formulae recognition based on the structural construction paradigm and two-dimensional grammars. In general, this approach can be successfully used in the anaylysis of inputs composed of objects that exhibit rich structural relations. An important benefit of the structural construction is in not treating symbols segmentation and structural anaylsis as two separate processes which allows the system to perform segmentation in the context of the whole formula structure, helping to solve arising ambiguities more reliably. We explore the opening provided by the polynomial complexity parsing algorithm and extend the grammar by many new grammar production rules which made the system useful for formulae met in the real world. We propose several grammar extensions to support a wide range of real mathematical formulae, as well as new features implemented in the application. Our current approach can recognize functions, limits, derivatives, binomial coefficients, complex numbers and more

    Character Recognition

    Get PDF
    Character recognition is one of the pattern recognition technologies that are most widely used in practical applications. This book presents recent advances that are relevant to character recognition, from technical topics such as image processing, feature extraction or classification, to new applications including human-computer interfaces. The goal of this book is to provide a reference source for academic research and for professionals working in the character recognition field

    Character-Aware Neural Language Models

    Full text link
    We describe a simple neural language model that relies only on character-level inputs. Predictions are still made at the word-level. Our model employs a convolutional neural network (CNN) and a highway network over characters, whose output is given to a long short-term memory (LSTM) recurrent neural network language model (RNN-LM). On the English Penn Treebank the model is on par with the existing state-of-the-art despite having 60% fewer parameters. On languages with rich morphology (Arabic, Czech, French, German, Spanish, Russian), the model outperforms word-level/morpheme-level LSTM baselines, again with fewer parameters. The results suggest that on many languages, character inputs are sufficient for language modeling. Analysis of word representations obtained from the character composition part of the model reveals that the model is able to encode, from characters only, both semantic and orthographic information.Comment: AAAI 201

    Classification-Based Screening of Parkinson’s Disease Patients through Graph and Handwriting Signals

    Get PDF
    Parkinson’s disease (PD) is one of the most common neurodegenerative diseases, affecting millions of people worldwide, especially among the elderly population. It has been demonstrated that handwriting impairment can be an important early marker for the detection of this disease. The aim of this study was to propose a simple and quick way to discriminate PD patients from controls through handwriting tasks using machine-learning techniques. We developed a telemonitoring system based on a user-friendly application for drawing tablets that enabled us to collect real-time information about position, pressure, and inclination of the digital pen during the experiment and, simultaneously, to supply visual feedback on the screen to the subject. We developed a protocol that includes drawing and writing tasks, including tasks in the Italian language, and we collected data from 22 healthy subjects and 9 PD patients. Using the collected signals and data from a preexisting database, we developed a machine-learning model to automatically discriminate PD patients from healthy control subjects with an accuracy of 77.5%

    Activity Report 2003

    Get PDF

    Feature Extraction Methods for Character Recognition

    Get PDF
    Not Include

    Activity Report 2002

    Get PDF
    corecore