Building a Personal Handwriting Recognizer on an Android Device

The wide usage of touch-screen based mobile devices has led to a large volume of the users preferring touch-based interaction with the machine, as opposed to traditional input via keyboards/mice. To exploit this, we focus on the Android platform to design a personalized handwriting recognition system that is acceptably fast, light-weight, possessing a user-friendly interface with minimally-intrusive correction and auto-personalization mechanisms. Since cursive writing on smaller screens is not usual, here we study non-cursive handwriting only. The recognition is done at character level using nearest-neighbor matching to a small, automatically user-adaptive and dynamically updating library of character-class template gestures.

[1]  Yang Li,et al.  Bootstrapping personal gesture shortcuts with the wisdom of the crowd and handwriting recognition , 2012, CHI.

[2]  Shigeki Sagayama,et al.  Substroke approach to HMM-based on-line Kanji handwriting recognition , 2001, Proceedings of Sixth International Conference on Document Analysis and Recognition.

[3]  Yang Li.  Gesture search: a tool for fast mobile data access , 2010, UIST '10.

[4]  Abdul Rahim Ahmad,et al.  Online handwriting recognition using support vector machine , 2004, 2004 IEEE Region 10 Conference TENCON 2004..

[5]  Ujjwal Bhattacharya,et al.  Unconstrained Bangla online handwriting recognition based on MLP and SVM , 2011, MOCR_AND '11.

[6]  Wei Jiang,et al.  HMM-based on-line multi-stroke sketch recognition , 2005, 2005 International Conference on Machine Learning and Cybernetics.

[7]  Bidyut Baran Chaudhuri,et al.  Online handwritten Bangla character recognition using HMM , 2008, 2008 19th International Conference on Pattern Recognition.

[8]  Bidyut Baran Chaudhuri,et al.  Online Bangla Word Recognition Using Sub-Stroke Level Features and Hidden Markov Models , 2010, 2010 12th International Conference on Frontiers in Handwriting Recognition.

[9]  Marcus Liwicki,et al.  Touch & Write: a multi-touch table with pen-input , 2010, DAS '10.

[10]  Shumin Zhai,et al.  SHARK2: a large vocabulary shorthand writing system for pen-based computers , 2004, UIST '04.

[11]  S. K. Parui,et al.  An Analytic Scheme for Online Handwritten Bangla Cursive Word Recognition , 2008 .

[12]  Jakob Sternby.  Structurally Based Template Matching of On-line Handwritten Characters , 2005, BMVC.

[13]  Marcus Liwicki,et al.  MCS for Online Mode Detection: Evaluation on Pen-Enabled Multi-touch Interfaces , 2011, 2011 International Conference on Document Analysis and Recognition.

[14]  Ujjwal Bhattacharya,et al.  On-line Handwriting Recognition of Indian Scripts - The First Benchmark , 2010, 2010 12th International Conference on Frontiers in Handwriting Recognition.

[15]  Juan Miguel Vilar,et al.  Improving a DTW-Based Recognition Engine for On-line Handwritten Characters by Using MLPs , 2009, 2009 10th International Conference on Document Analysis and Recognition.