Abstract:
Optical Character recognition is a very futile area of research in the field of image processing. Handwritten character recognition is the most challenging domain of OCR because every person tends to have his/her writing style. As a result, there is variance in every sample input taken from different users. Due to the presence of no standalone handwriting template and huge diversity of people's writing styles, an adaptive and effective character recognition module is required for efficiently identifying handwritten characters. On the other hand, Learning Vector Quantization or LVQ is a kind of supervised neural network which can learn and remember if proper training is provided. This paper focuses on constructing a Learning Vector Quantization based handwritten character recognition module which will be able to effectively identify different handwriting styles and recognize them with a significantly high degree of accuracy.
