How postal address readers are made adaptive

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2956, Issue , 2004, Pages 187-215

  Schäfer, Hartmut ^a   Bayer, Thomas ^a   Kreuzer, Klaus ^a   Miletzki, Udo ^a   Schambach, Marc Peter ^a   Schulte Austum, Matthias ^a  

^a Siemens Dematic AG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER SCIENCE; COMPUTERS;

ADAPTATION STRATEGIES; HIDDEN MARKOV; MAIL SORTING; REGIONS OF INTEREST; WRITTEN WORD;

CHARACTER RECOGNITION;

EID: 35048833773     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-24642-8_12     Document Type: Article

References (9)

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3. M.-P. SCHAMBACH. Model length adaptation of an HMM based cursive word recognition system. In Proc. of the 7th Int. Conf. on Document Analysis and Recognition, Edinburgh, Scotland, August 2003.
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7. M.-P. SCHAMBACH. Automatische Modellierung gebundener Handschrift in einem HMM-basierten Erkennungssystem. Proposed Dissertation, Universität Ulm, 2003.
8. JÜRGEN SCHÜRMANN. PATTERN CLASSIFICATION A Unified View of Statistical and Neural Approaches, Chapter 6.14 Recursive Learning pages 165-182. Wiley-Interscience, 1996.
9. K. OKUDA, E. TANAKA AND T. KASAI. A Method for the Correction of Garbled Words, based on the Levenshtein Metric IEEE Transactions on Computers, Vol. c-25, No. 2, February 1976