期刊名称：International Journal of Image Processing (IJIP)
电子版ISSN：1985-2304
出版年度：2014
卷号：8
期号：5
页码：313-324
出版社：Computer Science Journals
摘要：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.
关键词：Handwritten Character Recognition; Noise Reduction; Pre-processing Techniques In Character Recognition; Pattern Matching; Strokes; Fixed-language; Training Neural Networks; Gabor Filter.
