Universiti Teknologi Malaysia Institutional Repository

Writer independent online handwritten character recognition using a simple approach

Zafar, Muhammad Faisal and Mohamad, Dzulkifli and Othman, Muhamad Razib (2006) Writer independent online handwritten character recognition using a simple approach. Information Technology Journal, 5 (3). pp. 476-484. ISSN 1812-5638


Official URL: http://www.ansijournals.com/itj/2006/476-484.pdf


This study describes the simple approach involved in online handwriting recognition. Conventionally, the data obtained needs a lot of preprocessing including filtering, smoothing, slant removing and size normalization before recognition process. Instead of doing such lengthy preprocessing, this study presents a simple approach to extract the useful character information. The whole process requires no preprocessing and size normalization. This research evaluates the use of the Back-propagation Neural Network (BPN) and presents feature extraction mechanism in full detail to work with on-line handwriting recognition. The obtained recognition rates were 51 to 83% using the BPN for different sets of character samples. This study also describes a performance study in which a recognition mechanism with multiple thresholds is evaluated for back-propagation architecture. The results indicate that the application of multiple thresholds has significant effect on recognition mechanism. This is a writer-independent system and the method is applicable for off-line character recognition as well. The technique is tested for upper-case English alphabets for a number of different styles from different subjects.

Item Type:Article
Uncontrolled Keywords:character digitization, back-propagation neural networks, extreme coordinates, pattern recognition
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computer Science and Information System (Formerly known)
ID Code:8756
Deposited By: Dr Muhamad Razib Othman
Deposited On:21 Mar 2012 07:53
Last Modified:23 Oct 2017 08:11

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