Text/shape classifier for mobile applications with handwriting input

The paper provides a practical solution to a real-time text/shape differentiation problem for online handwriting input. The proposed structure of the classification system comprises stroke grouping and stroke classification blocks. A new set of features is derived that has low computational complexity. The method achieves 98.5 % text/shape classification accuracy on a benchmark dataset. The proposed stroke grouping machine learning approach improves classification robustness in relation to different input styles. In contrast to the threshold-based techniques, this grouping adaptation enhances the overall discriminating accuracy of the text/shape recognition system by 11.3 %. The solution improves system’s response on a touch-screen device.

[1]  Sargur N. Srihari,et al.  On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Cheng-Lin Liu,et al.  A robust approach to text line grouping in online handwritten Japanese documents , 2009, Pattern Recognit..

[3]  Randall Davis,et al.  Learning from Neighboring Strokes: Combining Appearance and Context for Multi-Domain Sketch Recognition , 2009, NIPS.

[4]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[5]  Tracy Anne Hammond,et al.  Using Entropy to Distinguish Shape Versus Text in Hand-Drawn Diagrams , 2009, IJCAI.

[6]  Sebastian Otte,et al.  Local Feature Based Online Mode Detection with Recurrent Neural Networks , 2012, 2012 International Conference on Frontiers in Handwriting Recognition.

[7]  Christopher M. Bishop,et al.  Distinguishing text from graphics in on-line handwritten ink , 2004, Ninth International Workshop on Frontiers in Handwriting Recognition.

[8]  Cheng-Lin Liu,et al.  Text/Non-text Classification in Online Handwritten Documents with Conditional Random Fields , 2012, CCPR.

[9]  Cheng-Lin Liu,et al.  Contextual text/non-text stroke classification in online handwritten notes with conditional random fields , 2014, Pattern Recognit..

[10]  XuLei Yang,et al.  Weighted support vector machine for data classification , 2005 .

[11]  DegtyarenkoIllya,et al.  Text/shape classifier for mobile applications with handwriting input , 2016 .

[12]  Kibok Lee,et al.  A flexible framework for online document segmentation by pairwise stroke distance learning , 2015, Pattern Recognit..

[13]  Masaki Nakagawa,et al.  Comparison of MRF and CRF for Text/Non-text Classification in Japanese Ink Documents , 2014, 2014 14th International Conference on Frontiers in Handwriting Recognition.

[14]  Thomas F. Stahovich,et al.  Grouping Strokes into Shapes in Hand-Drawn Diagrams , 2010, AAAI.

[15]  Yue Wang,et al.  Weighted support vector machine for data classification , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

[16]  Volkmar Frinken,et al.  Mode Detection in Online Handwritten Documents Using BLSTM Neural Networks , 2012, 2012 International Conference on Frontiers in Handwriting Recognition.

[17]  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.

[18]  Masaki Nakagawa,et al.  Text/Non-text Classification in Online Handwritten Documents with Recurrent Neural Networks , 2014, 2014 14th International Conference on Frontiers in Handwriting Recognition.

[19]  Nicole Vincent,et al.  A Set of Chain Code Based Features for Writer Recognition , 2009, 2009 10th International Conference on Document Analysis and Recognition.

[20]  Yong Wang,et al.  Building Digital Ink Recognizers Using Data Mining: Distinguishing between Text and Shapes in Hand Drawn Diagrams , 2010, IEA/AIE.

[21]  Marcus Liwicki,et al.  IAMonDo-database: an online handwritten document database with non-uniform contents , 2010, DAS '10.

[22]  Marcus Liwicki,et al.  Feature selection for on-line handwriting recognition of whiteboard notes , 2007 .

[23]  Guozhong Dai,et al.  Extraction and segmentation of tables from Chinese ink documents based on a matrix model , 2007, Pattern Recognit..

[24]  N. Vincent,et al.  A FRACTAL JUSTIFICATION OF THE NORMALIZATION STEP FOR ONLINE HANDWRITING RECOGNITION , 2004 .

[25]  Andrew McCallum,et al.  Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.

[26]  Josep Lladós,et al.  Categorization of Digital Ink Elements Using Spectral Features , 2007, GREC.

[27]  Cheng-Lin Liu,et al.  Multi-class segmentation of free-form online documents with tree conditional random fields , 2014, International Journal on Document Analysis and Recognition (IJDAR).