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Title: Overwriting repetition and crossing-out detection in online handwritten text
Authors: Bhattacharya N.
Frinken V.
Pal U.
Pratim Roy, Partha
Published in: Proceedings of 3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015
Abstract: Noise detection in online handwritten text is an important task for data acquisition. Noise occurs in online handwritten text in various ways. For example, crossing out the previously written text due to misspelling, repeated writing of the same stroke several times following a slightly different trajectory, simply writing corrections over other text are very common. Detection of these unwanted regions is a crucial pre-processing step in automatic text recognition. Currently detection and removal/correction of such regions are often done manually after collecting the data. Particularly for large databases, this can turn into a tedious and costly procedure. Consequently, in this work, we focus on noise detection for database creation. We propose to use different density-based features to distinguish between "relevant" and "unwanted" (or noisy) parts of writing. Using a 2-class HMM based classifier we get encouraging detection rate of unwanted regions from online handwritten text. © 2015 IEEE.
Citation: Proceedings of 3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015, (2016), 680- 684
Issue Date: 2016
Publisher: Institute of Electrical and Electronics Engineers Inc.
Keywords: Data acquisition
Pattern recognition
Database creation
Detection rates
Different densities
Large database
Noise detection
On-line handwritten text
Pre-processing step
Text recognition
Character recognition
ISBN: 9.78148E+12
Author Scopus IDs: 55604675700
Author Affiliations: Bhattacharya, N., Bose Institute, Kolkata, India
Frinken, V., Kyushu University, Japan
Pal, U., Indian Statistical Institute, Kolkata, India
Roy, P.P., Indian Institute of Technology, Roorkee, India
Appears in Collections:Conference Publications [CS]

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