Writer independent online handwritten character recognition using a simple approach

• Main Authors: Zafar, Muhammad Faisal, Mohamad, Dzulkifli, Othman, Muhamad Razib
• Format: Article
• Language: English
• Published: 2006
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://eprints.utm.my/id/eprint/8756/1/ITJ-v5-n3.pdf; http://eprints.utm.my/id/eprint/8756/; 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.