Building digital ink recognizers using data mining: Distinguishing between text and shapes in hand drawn diagrams

Rachel Blagojevic; Beryl Plimmer; John Grundy; Yong Wang

doi:10.1007/978-3-642-13022-9_36

Building digital ink recognizers using data mining: Distinguishing between text and shapes in hand drawn diagrams

Rachel Blagojevic, Beryl Plimmer, John Grundy, Yong Wang

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

3 Citations (Scopus)

Abstract

The low accuracy rates of text-shape dividers for digital ink diagrams are hindering their use in real world applications. While recognition of handwriting is well advanced and there have been many recognition approaches proposed for hand drawn sketches, there has been less attention on the division of text and drawing. The choice of features and algorithms is critical to the success of the recognition, yet heuristics currently form the basis of selection. We propose the use of data mining techniques to automate the process of building text-shape recognizers. This systematic approach identifies the algorithms best suited to the specific problem and generates the trained recognizer. We have generated dividers using data mining and training with diagrams from three domains. The evaluation of our new recognizer on realistic diagrams from two different domains, against two other recognizers shows it to be more successful at dividing shapes and text with 95.2% of strokes correctly classified compared with 86.9% and 83.3% for the two others.

Original language	English
Title of host publication	Trends in Applied Intelligent Systems - 23rd International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2010, Proceedings
Pages	358-367
Number of pages	10
Edition	PART 1
DOIs	https://doi.org/10.1007/978-3-642-13022-9_36
Publication status	Published - 1 Dec 2010
Externally published	Yes
Event	International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems 2010 - Cordoba Spain, Cordoba, Spain Duration: 1 Jun 2010 → 4 Jun 2010 Conference number: 23rd

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	6096 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems 2010
Abbreviated title	IEA/AIE 2010
Country/Territory	Spain
City	Cordoba
Period	1/06/10 → 4/06/10
Other	Old name (ERA event) = International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems

Keywords

pen-based interfaces
recognition algorithms
sketch recognition
Sketch tools

Access to Document

10.1007/978-3-642-13022-9_36

Cite this

Blagojevic, R., Plimmer, B., Grundy, J., & Wang, Y. (2010). Building digital ink recognizers using data mining: Distinguishing between text and shapes in hand drawn diagrams. In Trends in Applied Intelligent Systems - 23rd International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2010, Proceedings (PART 1 ed., pp. 358-367). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6096 LNAI, No. PART 1). https://doi.org/10.1007/978-3-642-13022-9_36

Blagojevic, Rachel ; Plimmer, Beryl ; Grundy, John et al. / Building digital ink recognizers using data mining : Distinguishing between text and shapes in hand drawn diagrams. Trends in Applied Intelligent Systems - 23rd International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2010, Proceedings. PART 1. ed. 2010. pp. 358-367 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

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title = "Building digital ink recognizers using data mining: Distinguishing between text and shapes in hand drawn diagrams",

abstract = "The low accuracy rates of text-shape dividers for digital ink diagrams are hindering their use in real world applications. While recognition of handwriting is well advanced and there have been many recognition approaches proposed for hand drawn sketches, there has been less attention on the division of text and drawing. The choice of features and algorithms is critical to the success of the recognition, yet heuristics currently form the basis of selection. We propose the use of data mining techniques to automate the process of building text-shape recognizers. This systematic approach identifies the algorithms best suited to the specific problem and generates the trained recognizer. We have generated dividers using data mining and training with diagrams from three domains. The evaluation of our new recognizer on realistic diagrams from two different domains, against two other recognizers shows it to be more successful at dividing shapes and text with 95.2\% of strokes correctly classified compared with 86.9\% and 83.3\% for the two others.",

keywords = "pen-based interfaces, recognition algorithms, sketch recognition, Sketch tools",

author = "Rachel Blagojevic and Beryl Plimmer and John Grundy and Yong Wang",

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doi = "10.1007/978-3-642-13022-9\_36",

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Blagojevic, R, Plimmer, B, Grundy, J & Wang, Y 2010, Building digital ink recognizers using data mining: Distinguishing between text and shapes in hand drawn diagrams. in Trends in Applied Intelligent Systems - 23rd International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2010, Proceedings. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 6096 LNAI, pp. 358-367, International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems 2010, Cordoba, Spain, 1/06/10. https://doi.org/10.1007/978-3-642-13022-9_36

Building digital ink recognizers using data mining: Distinguishing between text and shapes in hand drawn diagrams. / Blagojevic, Rachel; Plimmer, Beryl; Grundy, John et al.
Trends in Applied Intelligent Systems - 23rd International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2010, Proceedings. PART 1. ed. 2010. p. 358-367 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6096 LNAI, No. PART 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

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T2 - International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems 2010

AU - Blagojevic, Rachel

AU - Plimmer, Beryl

AU - Grundy, John

AU - Wang, Yong

N1 - Conference code: 23rd

PY - 2010/12/1

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N2 - The low accuracy rates of text-shape dividers for digital ink diagrams are hindering their use in real world applications. While recognition of handwriting is well advanced and there have been many recognition approaches proposed for hand drawn sketches, there has been less attention on the division of text and drawing. The choice of features and algorithms is critical to the success of the recognition, yet heuristics currently form the basis of selection. We propose the use of data mining techniques to automate the process of building text-shape recognizers. This systematic approach identifies the algorithms best suited to the specific problem and generates the trained recognizer. We have generated dividers using data mining and training with diagrams from three domains. The evaluation of our new recognizer on realistic diagrams from two different domains, against two other recognizers shows it to be more successful at dividing shapes and text with 95.2% of strokes correctly classified compared with 86.9% and 83.3% for the two others.

AB - The low accuracy rates of text-shape dividers for digital ink diagrams are hindering their use in real world applications. While recognition of handwriting is well advanced and there have been many recognition approaches proposed for hand drawn sketches, there has been less attention on the division of text and drawing. The choice of features and algorithms is critical to the success of the recognition, yet heuristics currently form the basis of selection. We propose the use of data mining techniques to automate the process of building text-shape recognizers. This systematic approach identifies the algorithms best suited to the specific problem and generates the trained recognizer. We have generated dividers using data mining and training with diagrams from three domains. The evaluation of our new recognizer on realistic diagrams from two different domains, against two other recognizers shows it to be more successful at dividing shapes and text with 95.2% of strokes correctly classified compared with 86.9% and 83.3% for the two others.

KW - pen-based interfaces

KW - recognition algorithms

KW - sketch recognition

KW - Sketch tools

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SN - 9783642130212

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Y2 - 1 June 2010 through 4 June 2010

ER -

Blagojevic R, Plimmer B, Grundy J, Wang Y. Building digital ink recognizers using data mining: Distinguishing between text and shapes in hand drawn diagrams. In Trends in Applied Intelligent Systems - 23rd International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2010, Proceedings. PART 1 ed. 2010. p. 358-367. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-642-13022-9_36

Building digital ink recognizers using data mining: Distinguishing between text and shapes in hand drawn diagrams

Abstract

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Keywords

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