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Krishnamoorthy, M.; And Others – Library Hi Tech, 1993
Describes a project conducted at Rensselaer Polytechnic Institute (New York) that developed methods for automatically subdividing pages from technical journals into smaller semantic units for transmission, display, and further processing in an electronic environment. Topics discussed include optical scanning and image compression, digital image…
Descriptors: Character Recognition, Optical Scanners, Scholarly Journals, Semantics

Rahn, Erwin – Library Hi Tech, 1984
Discusses the evolution of standards for bar codes (series of printed lines and spaces that represent numbers, symbols, and/or letters of alphabet) and describes the two types most frequently adopted by libraries--Code-A-Bar and CODE 39. Format of the codes is illustrated. Six references and definitions of terminology are appended. (EJS)
Descriptors: Character Recognition, Definitions, Information Retrieval, Library Automation

Tseng, Yuen-Hsien – Journal of the American Society for Information Science and Technology, 2001
Describes efforts in supporting information retrieval from OCR (optical character recognition) degraded text. Reports on approaches used in an automatic cataloging and searching contest for books in multiple languages, including a vector space retrieval model, an n-gram indexing method, and a weighting scheme; and discusses problems of Asian…
Descriptors: Character Recognition, Indexing, Information Retrieval, Languages

Grotophorst, Clyde W. – Library Hi Tech, 1989
Discusses the use of optical character recognition (OCR) technology to produce an ASCII text database. A tutorial on digital scanning and OCR is provided, and a systems integration project which used the Calera CDP-3000XF scanner and text retrieval software to construct a database of dissertations at George Mason University is described. (four…
Descriptors: Academic Libraries, Character Recognition, Computer Software, Databases

Henthorne, Eileen – Information Technology and Libraries, 1995
Describes a project at the Princeton University libraries that converted the pre-1981 public card catalog, using digital imaging and optical character recognition technology, to fully tagged and indexed records of text in MARC format that are available on an online database and will be added to the online catalog. (LRW)
Descriptors: Character Recognition, Databases, Indexing, Library Catalogs

Sun, Wei; And Others – Journal of the American Society for Information Science, 1992
Identifies types and distributions of errors in text produced by optical character recognition (OCR) and proposes a process using machine learning techniques to recognize and correct errors in OCR texts. Results of experiments indicating that this strategy can reduce human interaction required for error correction are reported. (25 references)…
Descriptors: Artificial Intelligence, Automation, Character Recognition, Error Correction

Pearce, Claudia; Nicholas, Charles – Journal of the American Society for Information Science, 1996
Presents experimentation results for the TELLTALE system, a dynamic hypertext environment that provides full-text search from a hypertext-style user interface for text corpora that may be garbled by OCR (optical character recognition) or transmission errors, and that may contain languages other than English. (Author/LRW)
Descriptors: Character Recognition, Full Text Databases, Hypermedia, Information Retrieval
Schantz, Herbert F. – Document Image Automation, 1993
Explains the value of neurocomputing as a unique and effective new technological concept for information processing and optical character recognition. Comparisons are made to digital computing and examples of applications such as recognizing handprinted characters are addressed. Products available from various companies are described. (Contains…
Descriptors: Character Recognition, Comparative Analysis, Computer Software, Digital Computers
Marsden, Jim – School Business Affairs, 1993
Desktop scanners can be used with a software technology called optical character recognition (OCR) to convert the text on virtually any paper document into an electronic form. OCR offers educators new flexibility in incorporating text into tests, lesson plans, and other materials. (MLF)
Descriptors: Character Recognition, Computer Peripherals, Computer Uses in Education, Elementary Secondary Education
Lazzaro, Joseph J. – Byte, 1993
Describes adaptive technology for personal computers that accommodate disabled users and may require special equipment including hardware, memory, expansion slots, and ports. Highlights include vision aids, including speech synthesizers, magnification, braille, and optical character recognition (OCR); hearing adaptations; motor-impaired…
Descriptors: Assistive Devices (for Disabled), Braille, Character Recognition, Computer Peripherals

Taghva, Kazem; And Others – Journal of the American Society for Information Science, 1994
Discusses the use of optical character recognition (OCR) for inputting documents in an information retrieval system and describes a study that used an OCR-generated database and its corresponding corrected version to examine query evaluation in the presence of noisy data. Scanning technology, recognition technology, and retrieval technology are…
Descriptors: Character Recognition, Data Processing, Databases, Evaluation Methods

McCullough, C. Sue – School Psychology Review, 1995
Focuses on research about application of text-to-speech systems in diagnosing and remediating word recognition, vocabulary knowledge, and comprehension disabilities. As school psychologists move toward a consultative model of service delivery, they need to know about technology such as speech synthesizers, digitizers, optical-character-recognition…
Descriptors: Character Recognition, Consultants, Integrated Learning Systems, Optical Scanners
Freeman, Raoul J. – Educational Computer, 1983
Describes system of computerized class registration utilizing bar code input which is part of the Student Data System, developed by Management Information Division of the Los Angeles Unified School District. An explanation of the system notes the hardware used, printing of bar code labels, registration procedures, and operational aspects. (EJS)
Descriptors: Character Recognition, Computer Programs, Data Processing, Elementary Secondary Education
Choudhury, G. Sayeed; DiLauro, Tim; Droettboom, Michael; Fujinaga, Ichiro; MacMillan, Karl; Nelson, Michael L.; Maly, Kurt; Thibodeau, Kenneth; Thaller, Manfred – D-Lib Magazine, 2001
These articles describe the experiences of the Johns Hopkins University library in digitizing their collection of sheet music; motivation for buckets, Smart Object, Dumb Archive (SODA) and the Open Archives Initiative (OAI), and initial experiences using them in digital library (DL) testbeds; requirements for archival institutions, the National…
Descriptors: Academic Libraries, Archives, Character Recognition, Computer Software
Burford, Anna M. – Journal of Business Education, 1980
Defines the components of the office of the future: data processing, micrographics, optical character recognition, telecommunications, and word processing. Also discusses teacher responsibility, student preparation, future challenges, and teacher awareness. (CT)
Descriptors: Character Recognition, Data Processing, Futures (of Society), Office Machines