Neural Networks are being used for character recognition from last many years but most of the work was confined to English character recognition. Till date, a very little work has been reported for Handwritten Farsi Character recognition. In this paper, we have made an attempt to recognize handwritten Farsi characters by using a multilayer perceptron with one hidden layer. The error backpropaga...

Word level training refers to the process of learning the parameters of a word recognition system based on word level criteria functions. Previously, researchers trained lexicon-driven handwritten word recognition systems at the character level individually. These systems generally use statistical or neural based character recognizers to produce character level confidence scores. In the case of...

Handwritten Character Recognition is an important part of Pattern Recognition. This is also referred to as Intelligent Character Recognition (ICR). In this paper, a conditional probability based combination of multiple recognizers for character recognition will be introduced. After preprocessing the given character image, different feature recognition algorithms are employed, and their performa...

Recognition of characters greatly depends upon the features used. Several features of the handwritten Arabic characters are selected and discussed. An off-line recognition system based on the selected features was built. The system was trained and tested with realistic samples of handwritten Arabic characters. Evaluation of the importance and accuracy of the selected features is made. The recog...

Recognition of Indian languages is a challenging problem. In Optical Character Recognition (OCR), acharacter or symbol to be recognized can be machine printed or handwritten characters/numerals. Several approaches in the past have been proposed that deal with problem of recognition of numerals/character depending on the type of feature extracted and way of extracting them. In this paper also a ...

The necessity of recognizing handwritten characters is increasing day by because its various applications. objective this paper to provide a sophisticated, effective and efficient way recognize classify Bangla characters. Here an extended convolutional neural network (CNN) model has been proposed Our CNN tested on “BanglalLekha-Isolated” dataset where there are 10 classes for digits, 11 vowels ...

The development of a pattern recognition architecture based on vector quantization techniques is presented which is applied to the recognition of handwritten bank forms. After an overview of nearest-neighbor classiication and clustering, a fast completely binary version of the k-means algorithm is introduced and results for large character databases are given. An integration of these methods in...