Abstract-Biometrics helps to identify individuals based on their physiological and behavioural characteristics, which can be used for their personal identifications. Various physical characteristics like retina patterns, iris patterns, fingerprint patterns, palmprint patterns, facial features etc. are utilized for such purposes. Palmprint recognition involves identifying an individual by matching the various principal lines, wrinkles and creases on the surface of the palm of the hand. The basis for using palmprint lies in the fact that since palmprint patterns are generated by random orientations of tissues and muscles of the hand during birth, no two individuals have exactly the same palmprint pattern. Advantages of using palmprint include the fact that such patterns remain more or less stable during one's lifetime and also that reliable images of the palm can be obtained quite easily using standard digital imaging techniques. Palmprint recognition uses the person's palm as a bio-metric for identifying or verifying person's identity. Palmprint patterns are a very reliable biometric and require minimum cooperation from the user for extraction. Palmprint is distinctive, easily captured by low resolution devices as well as contains additional features such as principal lines, wrinkles and ridges. Therefore it is suitable for everyone and it does not require any personal information of the user.

Advancement in Artificial Intelligence has lead to the developments of various "smart" devices. The biggest challenge in the field of image processing is to recognize documents both in printed and handwritten format. Character recognition is one of the most widely used biometric traits for authentication of person as well as document. Optical Character Recognition (OCR) is a type of document image analysis where scanned digital image that contains either machine printed or handwritten script input into an OCR software engine and translating it into an editable machine readable digital text format. A Neural network is designed to model the way in which the brain performs a particular task or function of interest. Each image character is comprised of 30×20 pixels. We have applied feature extraction technique for calculating the feature. Features extracted from characters are directions of pixels with respect to their neighboring pixels. These inputs are given to a back propagation neural network with hidden layer and output layer. We have used the Back propagation Neural Network for efficient recognition where the errors were corrected through back propagation and rectified neuron values were transmitted by feed-forward method in the neural network of multiple layers.

Abstract-Decision tree is an important method for both induction research and data mining, which is mainly used for model classification and prediction. ID3 algorithm is the most widely used algorithm in the decision tree so far. In this paper, the shortcoming of ID3's inclining to choose attributes with many values is discussed, and then a new decision tree algorithm which is improved version of ID3. In our proposed algorithm attributes are divided into groups and then we apply the selection measure 5 for these groups. If information gain is not good then again divide attributes values into groups. These steps are done until we get good classification/misclassification ratio. The proposed algorithms classify the data sets more accurately and efficiently.

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Abstract-Hidden Knowledge is very important in data mining field. Large data set have many hidden pattern which have very crucial information, Clustering is such technique which find the hidden pattern from the large data. Artificial Neural Network is very powerful tool in machine learning or in the field of computer visions. Competitive learning is used for Clustering in Neural network. Example of Competitive learning, SOM and ART are famous for clustering. SOM have the limitation of dimension, ART is good but computation cost is very high.