Advances in Character Recognition

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

Freeform User Interfaces for Graphical Computing

報告番号: 甲15222 ; 学位授与年月日: 2000-03-29 ; 学位の種別: 課程博士 ; 学位の種類: 博士(工学) ; 学位記番号: 博工第4717号 ; 研究科・専攻: 工学系研究科情報工学専

Stealthy Plaintext

Correspondence through email has become a very significant way of communication at workplaces. Information of most kinds such as text, video and audio can be shared through email, the most common being text. With confidential data being easily sharable through this method most companies monitor the emails, thus invading the privacy of employees. To avoid secret information from being disclosed it can be encrypted. Encryption hides the data effectively but this makes the data look important and hence prone to attacks to decrypt the information. It also makes it obvious that there is secret information being transferred. The most effective way would be to make the information seem harmless by concealing the information in the email but not encrypting it. We would like the information to pass through the analyzer without being detected. This project aims to achieve this by “encrypting” plain text by replacing suspicious keywords with non-suspicious English words, trying to keep the grammatical syntax of the sentences intact

Retrospective User Input Inference and Correction

Generally, the present disclosure is directed to retrospective user input inference and/or correction. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict intended user input based on actual user input

Applications of Natural Language Processing in Biodiversity Science

Centuries of biological knowledge are contained in the massive body of scientific literature, written for human-readability but too big for any one person to consume. Large-scale mining of information from the literature is necessary if biology is to transform into a data-driven science. A computer can handle the volume but cannot make sense of the language. This paper reviews and discusses the use of natural language processing (NLP) and machine-learning algorithms to extract information from systematic literature. NLP algorithms have been used for decades, but require special development for application in the biological realm due to the special nature of the language. Many tools exist for biological information extraction (cellular processes, taxonomic names, and morphological characters), but none have been applied life wide and most still require testing and development. Progress has been made in developing algorithms for automated annotation of taxonomic text, identification of taxonomic names in text, and extraction of morphological character information from taxonomic descriptions. This manuscript will briefly discuss the key steps in applying information extraction tools to enhance biodiversity science

The Effects of Word Prediction on Writing Fluency for Students with Physical Disabilities

Writing is a multifaceted, complex task that involves interaction between physical and cognitive skills. Individuals with physical disabilities vary in terms of both their physical and cognitive abilities. Often they must overcome one or more significant barriers in order to engage in the task of writing. Minimizing or eliminating barriers is important because opportunities are greater for individuals who can effectively communicate their ideas via writing. Assistive technology (AT) is an increasingly effective solution to increase typing fluency. The purpose of this study is to examine if word prediction software, a commonly used software program used with individuals with learning disabilities, will be effective for those with physical impairments to increase typing rate and reduce spelling errors (fluency). Data will be collected for words correct per minute (WCPM) and errors (e.g., spelling). Four middle- or high school-aged participants with diverse physical disabilities will be recruited in this single subject, alternating treatment design. Participants will type for three-minute timed sessions using either a standard word processor or Co:Writer 4000, a word prediction software program. Specific research questions are: (a) to what extent will students with physical and health disabilities produce greater WCPM when writing a draft paper on a common topic using word prediction rather than word processing, (b) to what extent will the use of word prediction software result in the production of different types of errors compared to errors produced using word processing, (c) to what extent will the use of word prediction software increase accuracy by decreasing spelling errors, (d) to what extent will more text be produced using word prediction software than with word processing, and (e) to what extent will word prediction increase motivation or willingness to write? Data will be graphed and analyzed for bifurcation. Bifurcation will be determined by examination of the means, level of performance, and trend. Finally, examination of errors will be used to verify spelling accuracy

Adaptive pattern recognition of hand-written Chinese characters using Fourier transforms of projection profiles

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Character Recognition

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