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Mixed Language Based Offline Handwritten Character Recognition Using First Stroke Based Training Sets
Magesh Kasthuri, V.Shanthi, Venkatasubramanian Sivaprasatham
Pages - 313 - 324     |    Revised - 10-08-2014     |    Published - 15-09-2014
Volume - 8   Issue - 5    |    Publication Date - September / October 2014  Table of Contents
Handwritten Character Recognition, Noise Reduction, Pre-processing Techniques In Character Recognition, Pattern Matching, Strokes, Fixed-language, Training Neural Networks, Gabor Filter.
Artificial Neural Network is an artificial representation of the human brain that tries to simulate its learning process. To train a network and measure how well it performs, an objective function must be defined. A commonly used performance criterion function is the sum of squares error function. Full end-to-end text recognition in natural images is a challenging problem that has recently received much attention in computer vision and machine learning. Traditional systems in this area have relied on elaborate models that incorporate carefully hand-engineered features or large amounts of prior knowledge. Language identification and interpretation of handwritten characters is one of the challenges faced in various industries. For example, it is always a big challenge in data interpretation from cheques in banks, language identification and translated messages from ancient script in the form of manuscripts, palm scripts and stone carvings to name a few. Handwritten character recognition using Soft computing methods like Neural networks is always a big area of research for long time and there are multiple theories and algorithms developed in the area of neural networks for handwritten character recognition.
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Mr. Magesh Kasthuri
WT - India
Professor V.Shanthi
Professor, Dept. of Computer Science St. Joseph’s College of Engineering Chennai, India - India
Professor Venkatasubramanian Sivaprasatham
Professor, Dept. of Information Technology, Nizwa College of Technology, Nizwa, Sultanate of Oman. - Oman