Mixed Language Based Offline Handwritten Character Recognition Using First Stroke Based Training Sets

International Journal of Image Processing

V.Shanthi¹  Venkatasubramanian Sivaprasatham¹  Magesh Kasthuri¹ 

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关键词: Handwritten Character Recognition;    Noise Reduction;    Pre-processing Techniques In Character Recognition;    Pattern Matching;    Strokes;    Fixed-language;    Training Neural Networks;    Gabor Filter.;   

DOI  :  

来源: 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.

【 授权许可】

Unknown