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Lightweight user-adaptive handwriting recognizer for resource constrained handheld devices

Authors:

A. Roy Chowdhury,

U. Bhattacharya,

S. K. ParuiAuthors Info & Claims

DAR '12: Proceeding of the workshop on Document Analysis and Recognition

Pages 114 - 119

https://doi.org/10.1145/2432553.2432574

Published: 16 December 2012 Publication History

Abstract

Here, we present our recent attempt to develop a lightweight handwriting recognizer suitable for resource constrained handheld devices. Such an application requires real-time recognition of handwritten characters produced on their touchscreens. The proposed approach is well suited for minimal user-lag on devices having only limited computing power in sharp contrast to standard laptops or desktop computers. Moreover, the approach is user-adaptive in the sense that it can adapt through user corrections to wrong predictions. With an increasing number of interactive corrections by the user, the recognition accuracy improves significantly. An input stroke is first re-sampled generating a fixed small number of sample points such that at most two critical points (points corresponding to high curvature) are preserved. We use their x- and y-coordinates as the feature vector and do not compute any other high-level feature vector. The squared Mahalanobis distance is used to identify each stroke of the input sample as one of several stroke categories pre-determined based on a large pool of training samples. The inverted covariance matrix and mean vector for a stroke class that are required for computing the Mahalanobis distance are pre-calculated and stored as Serialized Objects on the SD card of the device. A Look-Up Table (LUT) of stroke combinations as keys and corresponding character class as values is used for the final Unicode character output. In case of an incorrect character output, user corrections are used to automatically update the LUT adapting to the user's particular handwriting style.

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Cited By

Manakos ITechnitou EPetrou ZKarydas CTomaselli VVeronico GMountrakis G(2017)Multi-modal knowledge base generation from very high resolution satellite imagery for habitat mappingEuropean Journal of Remote Sensing10.5721/EuJRS2016495349:1(1033-1060)Online publication date: 17-Feb-2017
https://doi.org/10.5721/EuJRS20164953
Arduser LBissig PBrandes PWattenhofer R(2016)Recognizing text using motion data from a smartwatch2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)10.1109/PERCOMW.2016.7457172(1-6)Online publication date: Mar-2016
https://doi.org/10.1109/PERCOMW.2016.7457172
Das SShah Jaimeen H. Bora P(2013)Temporally scalable compression of animation geometry2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)10.1109/NCVPRIPG.2013.6776263(1-4)Online publication date: Dec-2013
https://doi.org/10.1109/NCVPRIPG.2013.6776263
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Index Terms

Lightweight user-adaptive handwriting recognizer for resource constrained handheld devices
1. Applied computing
  1. Computers in other domains
    1. Personal computers and PC applications
  2. Document management and text processing
    1. Document capture
      1. Optical character recognition

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Published In

cover image ACM Other conferences

DAR '12: Proceeding of the workshop on Document Analysis and Recognition

December 2012

162 pages

ISBN:9781450317979

DOI:10.1145/2432553

Program Chairs:
A. G. Ramakrishnan
IISc, Bangalore
,
Sitaram Ramachandrula
HP Labs, Bangalore

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 December 2012

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DAR '12: Workshop on Document Analysis and Recognition

December 16, 2012

Mumbai, India

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Cited By

Manakos ITechnitou EPetrou ZKarydas CTomaselli VVeronico GMountrakis G(2017)Multi-modal knowledge base generation from very high resolution satellite imagery for habitat mappingEuropean Journal of Remote Sensing10.5721/EuJRS2016495349:1(1033-1060)Online publication date: 17-Feb-2017
https://doi.org/10.5721/EuJRS20164953
Arduser LBissig PBrandes PWattenhofer R(2016)Recognizing text using motion data from a smartwatch2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)10.1109/PERCOMW.2016.7457172(1-6)Online publication date: Mar-2016
https://doi.org/10.1109/PERCOMW.2016.7457172
Das SShah Jaimeen H. Bora P(2013)Temporally scalable compression of animation geometry2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)10.1109/NCVPRIPG.2013.6776263(1-4)Online publication date: Dec-2013
https://doi.org/10.1109/NCVPRIPG.2013.6776263
Das ABhattacharya U(2013)ISIgraphy: A tool for online handwriting sample database generation2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)10.1109/NCVPRIPG.2013.6776181(1-4)Online publication date: Dec-2013
https://doi.org/10.1109/NCVPRIPG.2013.6776181

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