On-the-fly handwriting recognition using a high-level representation

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Original languageEnglish
Title of host publicationComputer Analysis of Images and Patterns - 16th International Conference, CAIP 2015, Proceedings
PublisherSpringer Verlag
Pages1-13
Number of pages13
ISBN (print)9783319231914
Publication statusPublished - 25 Aug 2015
Event16th International Conference on Computer Analysis of Images and Patterns, CAIP 2015 - Valletta, Malta
Duration: 2 Sept 20154 Sept 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9256
ISSN (Print)0302-9743
ISSN (electronic)1611-3349

Abstract

Automatic handwriting recognition plays a crucial role because writing with a pen is the most common and natural input method for humans. Whereas many algorithms detect the writing after finishing the input, this paper presents a handwriting recognition system that processes the input data during writing and thus detects misspelled characters on the fly from their origin. The main idea of the recognition is to decompose the input data into defined structures. Each character can be composed out of the structures point, line, curve, and circle. While the user draws a character, the digitized points of the pen are processed successively, decomposed into structures, and classified with the help of samples. The intermediate classification allows a direct feedback to the user as soon as the input differs from a given character.

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On-the-fly handwriting recognition using a high-level representation. / Reinders, Christoph; Baumann, Florian; Scheuermann, Björn et al.
Computer Analysis of Images and Patterns - 16th International Conference, CAIP 2015, Proceedings. Springer Verlag, 2015. p. 1-13 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9256).

Research output: Chapter in book/report/conference proceedingConference contributionResearchpeer review

Reinders, C, Baumann, F, Scheuermann, B, Ehlers, A, Mühlpforte, N, Effenberg, AO & Rosenhahn, B 2015, On-the-fly handwriting recognition using a high-level representation. in Computer Analysis of Images and Patterns - 16th International Conference, CAIP 2015, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9256, Springer Verlag, pp. 1-13, 16th International Conference on Computer Analysis of Images and Patterns, CAIP 2015, Valletta, Malta, 2 Sept 2015. https://doi.org/10.1007/978-3-319-23192-1_1
Reinders, C., Baumann, F., Scheuermann, B., Ehlers, A., Mühlpforte, N., Effenberg, A. O., & Rosenhahn, B. (2015). On-the-fly handwriting recognition using a high-level representation. In Computer Analysis of Images and Patterns - 16th International Conference, CAIP 2015, Proceedings (pp. 1-13). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9256). Springer Verlag. https://doi.org/10.1007/978-3-319-23192-1_1
Reinders C, Baumann F, Scheuermann B, Ehlers A, Mühlpforte N, Effenberg AO et al. On-the-fly handwriting recognition using a high-level representation. In Computer Analysis of Images and Patterns - 16th International Conference, CAIP 2015, Proceedings. Springer Verlag. 2015. p. 1-13. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-23192-1_1
Reinders, Christoph ; Baumann, Florian ; Scheuermann, Björn et al. / On-the-fly handwriting recognition using a high-level representation. Computer Analysis of Images and Patterns - 16th International Conference, CAIP 2015, Proceedings. Springer Verlag, 2015. pp. 1-13 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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abstract = "Automatic handwriting recognition plays a crucial role because writing with a pen is the most common and natural input method for humans. Whereas many algorithms detect the writing after finishing the input, this paper presents a handwriting recognition system that processes the input data during writing and thus detects misspelled characters on the fly from their origin. The main idea of the recognition is to decompose the input data into defined structures. Each character can be composed out of the structures point, line, curve, and circle. While the user draws a character, the digitized points of the pen are processed successively, decomposed into structures, and classified with the help of samples. The intermediate classification allows a direct feedback to the user as soon as the input differs from a given character.",
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AU - Reinders, Christoph

AU - Baumann, Florian

AU - Scheuermann, Björn

AU - Ehlers, Arne

AU - Mühlpforte, Nicole

AU - Effenberg, Alfred Oliver

AU - Rosenhahn, Bodo

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AB - Automatic handwriting recognition plays a crucial role because writing with a pen is the most common and natural input method for humans. Whereas many algorithms detect the writing after finishing the input, this paper presents a handwriting recognition system that processes the input data during writing and thus detects misspelled characters on the fly from their origin. The main idea of the recognition is to decompose the input data into defined structures. Each character can be composed out of the structures point, line, curve, and circle. While the user draws a character, the digitized points of the pen are processed successively, decomposed into structures, and classified with the help of samples. The intermediate classification allows a direct feedback to the user as soon as the input differs from a given character.

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