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Humans are comfortable with pen and papers for authentication and authorization in legal transactions. In this case it is very much essential that a person’s Hand written signature to be identified uniquely. The development of efficient technique is to extract features from Handwritten Signature Image and verify the signature with higher accuracy. This paper presents a method for off line hand written signature verification with higher accuracy. In this paper we have introduced a procedure to extract features from Handwritten Signature Images. That computed feature is used for verification. Here we used a clustering technique for verification.

The strategic and contemporary importance of e-governance has been recognized across the world. In India too, various ministries of Govt. of India and State Governments have taken e-governance initiatives to provide e-services to citizens and the business they serve. To achieve the mission objectives, and make such e-governance initiatives successful it would be necessary to improve the trust and confidence of the stakeholders. It is assumed that the delivery of government services will share the same public network information that is being used in the community at large. In particular, the Internet will be the principal means by which public access to government and government services will be achieved. To provide the security measures main aim is to identify user’s access requirement for the stakeholders and then according to the models of Nath’s approach. Based on this analysis, the Govt. can also make standards of security based on the e-governance models. Thus there will be less human errors and bias. This analysis leads to the security architecture of the specific G2C application.

In this paper, we propose a method to identify abnormal growth of cells in breast tissue and suggest further pathological test, if necessary. We compare normal breast tissue with malignant invasive breast tissue by a series of image processing steps. Normal ductal epithelial cells and ductal / lobular invasive carcinogenic cells also consider for comparison here in this paper. In fact, features of cancerous breast tissue (invasive) are extracted and analyses with normal breast tissue. We also suggest the breast cancer recognition technique through image processing and prevention by controlling p53 gene mutation to some greater extent.

The trees are basically identified by their leaves. There are different varieties of trees grown throughout the world. Some are important cash crop. Some are used in medicine. The tree identification is very important in day to day life. Their identifications had been studied using various laboratory methods. The morphological and genetically characteristics were employed to classify different leafs. However, the presence of wide morphological varieties through evolution among the various leaf cultivars made it more complex and difficult to classify them. Therefore manual identification as well as classification of these leaves is a tedious task. During the last few decades computational biologists have studied various diversities among leaf due to huge number of evolutionary changes. Leaf structures play a very crucial role in determining the characteristics of a plant. The broad and narrow shaped leaves, leaf arrangement, leaf margin characteristics features which differentiate various leaf of a tree. This project proposed the methods to identify the leaf using an image analysis based approach.

Transferring information from one part to another of the world is the main aim of communication. Now a day, the information is available in forms of documents or files created on requirements basis. The more the requirements the large the documents are. That is why; the way of creation which is random in nature as well as storage bends the documents unstructured in nature. The result is that, dealing with these documents becomes a headache. For the ease of process, the frequently required data should maintain certain pattern. But being unfortunate enough, most of the time we have to face problems like erroneous data retrieving or modification anomalies or even a large amount of time may be given for retrieving a single document. To overcome the situation, a solution has raised named unstructured document categorization. This field is a vast one containing all kind of solutions for various type of document categorization. Basically, the documents which are unstructured in nature will be categorized based on some given constraints. And through this paper we would like to highlight the most as well as popular techniques like text and data mining, genetic algorithm, lexical chaining, binarization methods in the field of unstructured document categorization so that we can reach the fulfillment of desired unstructured document categorization.

In this paper, a new technique for hiding the data of images has been proposed. This method is invented to hide an image file entirely with in another image file keeping two considerations in mind which are Size and Degree of Security. At the source, the image which is to be hidden (target image) is encoded at the end of another image (cover image). Double layer security of the hidden image can be achieved (over the untrusted network) by; firstly, the starting point of encoding the image data is depended on the size of the images and it is stored within the encoded image at the end of its header information as a cipher text.; secondly, the target image is hidden behind the cover image by following our encrypted image hiding technique.

In this paper we propose Handwritten Signature Classification using supervised chromosome clustering technique. Due to the time variant nature of handwriting of human being, a set of hundred sample Handwritten Signatures first collected from the user or individual in form of same sized grayscale images. These grayscale handwritten signature images will be used as the training set in our classification algorithm. Our propose algorithm will then decide whether the future incoming handwritten signature of an individual can be a member of the training set or not. In this paper, distance and similarities play an important role, where the greater the dissimilarity measure or distance of genes, the more dissimilar are the two chromosomes.

In this paper we propose a new biometric-based Iris feature extraction system. The system automatically acquires the biometric data in numerical format (Iris Images) by using a set of properly located sensors. We are considering camera as a high quality sensor. Iris Images are typically color images that are processed to gray scale images. Then the Feature extraction algorithm is used to detect “IRIS Effective Region (IER)” and then extract features from “IRIS Effective Region (IER)” that are numerical characterization of the underlying biometrics. Later on this work will be helping to identify an individual by comparing the feature obtained from the feature extraction algorithm with the previously stored feature by producing a similarity score. This score will be indicating the degree of similarity between a pair of biometrics data under consideration. Depending on degree of similarity, individual can be identified.

Information Extraction (IE) is a type of information retrieval meant for extracting structured information. In general, the information on the web is well structured in HTML or XML format. And IE will be there to structure these documents, by using learning techniques for pattern matching in the content. A typical application of IE is to scan a set of documents written in a natural language and populate a database with the information extracted. In this paper, we have concentrated our research work to give a heuristic approach for interactive information extraction technique where the information is in Indian Regional Language. This enables any naive user to extract regional language (Indian) based document from a web document efficiently. It is just similar to a pre-programmed information extraction engine.

Handwritten Signature Identification is a classical work area in the line of Computer Science and Technology since last few years. Various new techniques of Image Analysis also attracting the Computer Scientists as well. Firstly, Pixel clustering is used to transform the signature image into bi-color image. Then secondly, instead of considering the whole image, only signature area is extracted. Thirdly, by using Image scaling technique the signature image resized along the coordinate directions. As different techniques are used to subsample (image after transformation) which will be discussed in turn. Fourthly, a different technique is used for thinning to reduce the threshold output of an edge detector algorithm is used to lines of a single pixel thickness. In this paper we propose the above mentioned series of techniques as the preprocessing analysis part of Handwritten Signature Recognition.