105 research outputs found

    Online Handwritten Chinese/Japanese Character Recognition

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    Pen pressure features for writer-independent on-line handwriting recognition based on substroke HMM

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    This paper discusses the use of pen pressure as a feature in writer-independent on-line handwriting recognition. We propose two kinds of features related to pen pressure: one is the pressure representing pen ups and downs in a continuous manner; the other is the time-derivative of the pressure representing the temporal pattern of the pen pressure. Combining either of them with the existing feature (velocity vector), a 3-dimensional feature is composed for character recognition. Some techniques of interpolating the pen pressure during the pen-up interval is also proposed for a pre-processing purpose. Through experimental evaluation using 1,016 elementary Kanji characters compared with the baseline performance using velocity vector only, the additional use of pen pressure improved the performance from 97.5% to 98.1% for careful writings and from 91.1% to 93.1% for cursive writings

    Statistical Deformation Model for Handwritten Character Recognition

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    Recognition of Japanese handwritten characters with Machine learning techniques

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    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

    Advances in Character Recognition

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    This book presents advances in character recognition, and it consists of 12 chapters that cover wide range of topics on different aspects of character recognition. Hopefully, this book will serve as a reference source for academic research, for professionals working in the character recognition field and for all interested in the subject

    SCML: A Structural Representation for Chinese Characters

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    Chinese characters are used daily by well over a billion people. They constitute the main writing system of China and Taiwan, form a major part of written Japanese, and are also used in South Korea. Anything more than a cursory glance at these characters will reveal a high degree of structure to them, but computing systems do not currently have a means to operate on this structure. Existing character databases and dictionaries treat them as numerical code points, and associate with them additional `hand-computed\u27 data, such as stroke count, stroke order, and other information to aid in specific searches. Searching by a character\u27s `shape\u27 is effectively impossible in these systems. I propose a new approach to representing these characters, through an XML-based language called SCML. This language, by encoding an abstract form of a character, allows the direct retrieval of important information such as stroke count and stroke order, and permits useful but previously impossible automated analysis of characters. In addition, the system allows the design of a view that takes abstract SCML representations as character models and outputs glyphs based on an aesthetic, facilitating the creation of `meta-fonts\u27 for Chinese characters. Finally, through the creation of a specialized database, SCML allows for efficient structural character queries to be performed against the body of inserted characters, thus allowing people to search by the most obvious of a character\u27s characteristics: its shape

    Template Based Recognition of On-Line Handwriting

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    Software for recognition of handwriting has been available for several decades now and research on the subject have produced several different strategies for producing competitive recognition accuracies, especially in the case of isolated single characters. The problem of recognizing samples of handwriting with arbitrary connections between constituent characters (emph{unconstrained handwriting}) adds considerable complexity in form of the segmentation problem. In other words a recognition system, not constrained to the isolated single character case, needs to be able to recognize where in the sample one letter ends and another begins. In the research community and probably also in commercial systems the most common technique for recognizing unconstrained handwriting compromise Neural Networks for partial character matching along with Hidden Markov Modeling for combining partial results to string hypothesis. Neural Networks are often favored by the research community since the recognition functions are more or less automatically inferred from a training set of handwritten samples. From a commercial perspective a downside to this property is the lack of control, since there is no explicit information on the types of samples that can be correctly recognized by the system. In a template based system, each style of writing a particular character is explicitly modeled, and thus provides some intuition regarding the types of errors (confusions) that the system is prone to make. Most template based recognition methods today only work for the isolated single character recognition problem and extensions to unconstrained recognition is usually not straightforward. This thesis presents a step-by-step recipe for producing a template based recognition system which extends naturally to unconstrained handwriting recognition through simple graph techniques. A system based on this construction has been implemented and tested for the difficult case of unconstrained online Arabic handwriting recognition with good results

    A Sketch-Based Educational System for Learning Chinese Handwriting

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    Learning Chinese as a Second Language (CSL) is a difficult task for students in English-speaking countries due to the large symbol set and complicated writing techniques. Traditional classroom methods of teaching Chinese handwriting have major drawbacks due to human experts’ bias and the lack of assessment on writing techniques. In this work, we propose a sketch-based educational system to help CSL students learn Chinese handwriting faster and better in a novel way. Our system allows students to draw freehand symbols to answer questions, and uses sketch recognition and AI techniques to recognize, assess, and provide feedback in real time. Results have shown that the system reaches a recognition accuracy of 86% on novice learners’ inputs, higher than 95% detection rate for mistakes in writing techniques, and 80.3% F-measure on the classification between expert and novice handwriting inputs
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