Mining Vibrational Effects on Off-line Handwriting Recognition


  • L.C. Wong School of Mechanical Engineering, Engineering Campus, Universiti Sains Malaysia, 14300 Nibong Tebal, Penang, Malaysia.
  • W.P. Loh School of Mechanical Engineering, Engineering Campus, Universiti Sains Malaysia, 14300 Nibong Tebal, Penang, Malaysia.


Classification, Handwriting Recognition, Offline Handwriting, Parkinson’s Disease, Vibrational Stress,


An individual’s handwriting exhibits variation under external factors, such as writing surface, writing pen, and writing force. Recent studies on handwriting recognition emphasised on interpretation techniques using feature extraction, pattern recognition, and classification approaches. However, no study has evaluated the effects of external source vibrations on handwriting patterns. Hence, this study analyses offline handwritings features on two conditions: with vibrational (V) and without vibrational (N) stresses using the data mining approach. The goal was mainly to recognise individual handwriting features characterised by vibrational conditions. This research was performed on experimental and public offline handwriting databases consisting of English phrases written under (V) and (N) conditions. Vibrational stresses impact was simulated with Mondial Slim Beauty Fitness Massager strapped onto the writing table and Parkinson’s Disease (PD) patient with hand tremor symptom. Nine handwriting size metrics with demographic data were extracted as the data attributes. PART and J48 classification algorithms in Waikato Environment for Knowledge Analysis (WEKA) tool were employed on cross-validation and full training set modes to classify the handwriting data into two predefined classes: (V) and (N). Further significant attributes that distinguish data classes were examined on the decision list and tree diagram constructed from PART and J48. Findings showed that size of “short” letter and “tail” letter were dominant to determine handwriting classes at accuracies: 55.3%- 66.7% (crossvalidation) and 86.0% - 100.0% (training set). The study suggests that the size of “short” letter and “tail” letter are the dominant features to distinguish between the (V) and (N) handwriting.


R. Plamondon and S. N. Srihari, “On-Line and Off-Line Handwriting Recognition : A Comprehensive Survey,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 1, pp. 63–84, 2000.

F. Zamora-Martínez, V. Frinken, S. España-Boquera, M. J. CastroBleda, A. Fischer, and H. Bunke, “Neural Network Language Models for Off-line Handwriting Recognition,” Pattern Recognit., vol. 47, no. 4, pp. 1642–1652, 2014.

K. Jayech, M. A. Mahjoub, and N. E. Ben Amara, “Synchronous Multi-Stream Hidden Markov Model for Offline Arabic Handwriting Recognition without Explicit Segmentation,” Neurocomputing, vol. 214, pp. 958–971, 2016.

P. M. Kamble and R. S. Hegadi, “Handwritten Marathi Character Recognition using R-HOG Feature,” Procedia Comput. Sci., vol. 45, no. C, pp. 266–274, 2015.

E. N. Zois, L. Alewijnse, and G. Economou, “Offline Signature Verification and Quality Characterization using Poset-oriented Grid Features,” Pattern Recognit., vol. 54, pp. 162–177, 2016.

S. H. Chang, C. L. Chen, and N. Y. Yu, “Biomechanical Analyses of Prolonged Handwriting in Subjects with and without Perceived Discomfort,” Hum. Mov. Sci., vol. 43, no. 8, pp. 1–8, 2015.

H. M. Hsu, Y. C. Lin, W. J. Lin, C. J. Lin, Y. L. Chao, and L. C. Kuo, “Quantification Of Handwriting Performance: Development of a Force Acquisition Pen for Measuring Hand-grip and Pen Tip Forces,” Meas. J. Int. Meas. Confed., vol. 46, no. 1, pp. 506–513, 2013.

P. H. T. Q. de Almeida, D. M. C. da Cruz, L. A. Magna, and I. S. V. Ferrigno, “An Electromyographic Analysis of Two Handwriting Grasp Patterns,” J. Electromyogr. Kinesiol., vol. 23, no. 4, pp. 838–843, 2013.

A. Choudhary, R. Rishi, and S. Ahlawat, “Off-line Handwritten Character Recognition Using Features Extracted from Binarization Technique,” AASRI Procedia, vol. 4, pp. 306–312, 2013.

K. Assaleh, T. Shanableh, and H. Hajjaj, “Recognition of handwritten Arabic alphabet via hand motion tracking,” J. Franklin Inst., vol. 346, no. 2, pp. 175–189, 2009.

I. Agrawal, A. Vashishtha, and R. Kumar, “Slant Angle Estimation in Handwritten Documents,” Int. J. Comput. Sci. Manag. Stud., vol. 14, no. 5, 2014.

P. Joshi, A. Agarwal, A. Dhavale, R. Suryavanshi, and S. Kodolika, “Handwriting Analysis for Detection of Personality Traits using Machine Learning Approach,” Int. J. Comput. Appl. (0975 – 8887), vol. 130, no. 15, pp. 40–45, 2015.

O. Surinta, M. F. Karaaba, L. R. B. Schomaker, and M. A. Wiering, “Recognition of Handwritten Characters using Local Gradient Feature Descriptors,” Eng. Appl. Artif. Intell., vol. 45, pp. 405–414, 2015.

Á. Morera, Á. Sánchez, J. F. Vélez, and A. B. Moreno, “Gender and Handedness Prediction from Offline Handwriting Using Convolutional Neural Networks,” vol. 2018, 2018.

Y. Chherawala, P. P. Roy, and M. Cheriet, “Combination of Context-dependent Bidirectional Long Short-term Memory Classifiers for Robust Offline Handwriting Recognition,” Pattern Recognit. Lett., vol. 90, pp. 58–64, 2017.

N. Zhi, “Quantitative Assessment of Micrographia and Tremor in Static Handwriting Samples,” 2016.

B. Ribaudo, “The ABC’s of Parkinson’s Disease Handwriting,” 2012. [Online]. Available:

D. Sujitha, “To Analysis of a Hand Writing Recognition using KNearest Neighbour (KNN), Neural Network (NN) and Decision Tree Classifiers,” Int. J. Comput. Sci. Mob. Comput., vol. 4, no. 7, pp. 351–357, 2015.

A. Bal and R. Saha, “An Improved Method for Handwritten Document Analysis Using Segmentation, Baseline Recognition and Writing Pressure Detection,” Procedia Comput. Sci., vol. 93, no. September, pp. 403–415, 2016.

S. Impedovo, “More than Twenty Years of Advancements on Frontiers in Handwriting Recognition,” Pattern Recognit., vol. 47, no. 3, pp. 916–928, 2014.




How to Cite

Wong, L., & Loh, W. (2018). Mining Vibrational Effects on Off-line Handwriting Recognition. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(3-2), 71–75. Retrieved from