A Global Online Handwriting Recognition Approach Based on Frequent Patterns

C. Gmati, H. Amiri

Abstract


In this article, the handwriting signals are represented based on geometric and spatio-temporal characteristics to increase the feature vectors relevance of each object. The main goal was to extract features in the form of a numeric vector based on the extraction of frequent patterns. We used two types of frequent motifs (closed frequent patterns and maximal frequent patterns) that can represent handwritten characters pertinently. These common features patterns are generated from a raw data transformation method to achieve high relevance. A database of words consisting of two different letters was created. The proposed application gives promising results and highlights the advantages that frequent pattern extraction algorithms can achieve, as well as the central role played by the “minimum threshold” parameter in the overall description of the characters.


Keywords


frequent features; mining frequent patterns; spatio-temporal relations; minimum threshold; online handwriting recognition

Full Text:

PDF

References


I. Degtyarenko, O. Radyvonenko, K. Bokhan, V. Khomenko, “Text/shape classifier for mobile applications with handwriting input”, International Journal on Document Analysis and Recognition, Vol. 19, No. 4, pp. 369-379, 2016

N. Dounskaia, A. W. Van Gemmert, B. C. Leis, G. E. Stelmach, “Biased wrist and finger coordination in Parkinsonian patients during performance of graphical tasks”, Neuropsychologia, Vol. 47, No. 12, pp. 2504-2514, 2009

M. S. Julius, R. Meir, Z. Shechter-Nissim, E. Adi-Japha, “Children's ability to learn a motor skill is related to handwriting and reading proficiency”, Learning and Individual Differences, Vol. 51, pp. 265-272, 2016

J. Shin, T. Okuyama, “Detection of alcohol intoxication via online handwritten signature verification”, Pattern Recognition Letters , Vol. 35, pp. 101–104, 2014

V. Paz-Villagrán, J. Danna, J.-L. Velay, “Lifts and stops in proficient and dysgraphic handwriting”, Human Movement Science, Vol. 33, pp. 381-394, 2014

T. Deselaers, D. Keysers, J. Hosang, H. A. Rowley, “GyroPen: Gyroscopes for Pen-Input With Mobile Phones”, IEEE Transactions on Human-Machine Systems, Vol. 45, No. 2, pp. 263-271, 2015

M. Young, The Technical Writer’s Handbook. Mill Valley, CA: University Science, 1989

B. Q. Huang, Y. B. Zhang, M. T. Kechadi, “Preprocessing Techniques for Online Handwriting Recognition”, Seventh International Conference on Intelligent Systems Design and Applications (ISDA 2007), Rio de Janeiro, Brazil, pp. 793-800, October 20-24, 2007

M. A. Abuzaraida, A. M. Zeki, A. M. Zeki, “Online Recognition System for Handwritten Hindi Digits Based on Matching Alignment Algorithm”, 3rd International Conference on Advanced Computer Science Applications and Technologies, Amman, Jordan, pp. 168-171, December 29-30, 2014

M. E. Mustafa, H. A. A. Alshafy, “Characters' boundaries based segmentation for online Arabic handwriting”, International Conference on Computing, Electrical and Electronic Engineering (ICCEEE), Khartoum, Sudan, pp. 306-310, August 26-28, 2013

C. De Stefano, M. Garruto, A. Marcelli, “A multiresolution approach to on-line handwriting segmentation and feature extraction”, IEEE 17th International Conference on Pattern Recognition (ICPR 2004), Vol. 2, pp. 614-617, 2004

Y. Jiang, X. Wang, X. Ao, G. Dai, “Online Recognition of Handwritten Chemical Formula”, 2nd Joint Conference on Harmonious Human Machine Environment. Hangzhou, China, pp. 111-115, 2006

L. Zhao, H. Yan, G. Shi, J. Yang, “Segmentation of Connected Symbols in Online Handwritten Chemical Formulas”, International Conference on System Science, Engineering Design and Manufacturing Informatization (ICSEM), Yichang, China, pp. 278-281, November 12-14, 2010

M. Cheriet, N. Kharma, C. Liu, C. Suen, Character Recognition Systems: A Guide for Students and Practitioners, John Wiley & Sons, 2007

O. Maimon, L. Rokach, Data Mining and Knowledge Discovery Handbook, Springer Science+Business Media, Inc, 2005

U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, R. Uthurusamy Advances in Knowledge Discovery and Data Mining, AAAI Press, 1996

S. Mitra, T. Acharya, Data Mining Multimedia, Soft Computing and Bioinformatics, John Wiley & Sons, 2003

R. Agrawal, T. Imielinski, A. Swami, “Mining Association Rules Between Sets of Items in Large Databases”, in: SIGMOD '93: Proceedings of the 1993 ACM SIGMOD international conference on Management of data, pp. 207-216, ACM, 1993

M. J. Zaki, C.-J. Hsiao, “ChARM: An efficient algorithm for closed itemset mining”, in: 2002 SIAM International Conference on Data Mining, pp. 457-473, SIAM, 2002

L. Szathmary, “Symbolic Data Mining Methods with the Coron Platform”, PhD Thesis, Henri Poincaré University, 2002

S. Dutta Chowdhury, U. Bhattacharya, S. K. Parui, “Online Handwriting Recognition Using Levenshtein Distance Metric”, 12th International Conference on Document Analysis and Recognition, Washington DC, USA, August 25-28, 2013

M. Mori, S. Uchida, H. Sakano, “Global feature for online character recognition”, Pattern Recognition Letters, Vol. 35, pp. 142-148, 2013

S. Dewangan, P. K. Gupta, U. K. Sahu, I. K. Verma, “Realtime Recognition of Handwritten Words using Hidden Markov Model”, International Journal of Technological Synthesis and Analysis, Vol. 1, No. 1, pp. 7-9, 2012

V. Vuori, M. Aksela, J. Laaksonen, E. Oja, “On-line recognition of handwritten characters”, in: Biennial Report, Laboratory of Computer and Information Science, Neural Networks Research Centre, Helsinki University of Technology, 2003

N. B. Amara, A. Belaïd, N. Ellouze, “Utilisation des modèles markoviens en reconnaissance de l'écriture arabe : état de l'art”, Colloque International Francophone sur l'Ecrit et le Document - CIFEd'00, Lyon, France, July, 2000

K. P. Primekumar, S. M. Idiculla, “On-line Malayalam Handwritten Character Recognition using HMM and SVM”, International Conference on Signal Processing, Image Processing and Pattern Recognition (ICSIPR), Coimbatore, India, February 7-8, 2013

S.-J. Cho, J. H. Kim, “A Bayesian Network Approach for On-line Handwriting Recognition”, in: Digital Document Processing. Advances in Pattern Recognition, pp. 121-141, 2007

N. Tagougui, H. Boubaker, M. Kherallah, A. M. Alimi, “A hybrid MLPNN/HMM recognition system for online Arabic Handwritten script”, World Congress on Computer and Information Technology (WCCIT), Sousse, Tunisia, June 22-24, 2013

H. El Abed, M. Kherallah, V. Margner, A. M. Alimi, “On-line Arabic handwriting recognition competition: ADAB database and participating systems”, International Journal on Document Analysis and Recognition, Vol. 14, No. 1, pp. 15-23, 2011

I. Ota, R. Yamamoto, S. Sako, S. Sagayama,“On-line Handwritten Kanji Recognition Based on Inter-stroke Grammar”, IEEE 9th International Conference on Document Analysis and Recognition (ICDAR 2007), Vol. 2, pp. 1188-1192, 2007

F. Alvaro, J.-A. Sanchez, J.-M. Benedí, “Recognition of on-line handwritten mathematical expressions using 2D stochastic context-free grammars and hidden Markov models”, Pattern Recognition Letters, Vol. 35, pp. 58-67, 2014




eISSN: 1792-8036     pISSN: 2241-4487