Satish R. Kolhe

Offline Recognition of Devanagari Script: A Survey

In India, more than 300 million people use Devanagari script for documentation. There has been a significant improvement in the research related to the recognition of printed as well as handwritten Devanagari text in the past few years. State of the art from 1970s of machine printed and handwritten Devanagari optical character recognition (OCR) is discussed in this paper. All feature-extraction techniques as well as training, classification and matching techniques useful for the recognition are discussed in various sections of the paper. An attempt is made to address the most important results reported so far and it is also tried to highlight the beneficial directions of the research till date. Moreover, the paper also contains a comprehensive bibliography of many selected papers appeared in reputed journals and conference proceedings as an aid for the researchers working in the field of Devanagari OCR.

Dynamic Time Warping Based Static Hand Printed Signature Verification

Static signature verication has a signicant use in establishing the authenticity of bank checks, insurance and legal documents based on the signatures they carry. As an individual signs only a few times on the forms for opening an account with any bank or for insurance related purposes, the number of genuine signature templates available in banking and insurance applications is limited, a new approach of static handwritten signature verication based on Dynamic Time Warping (DTW) by using only ve genuine signatures for training is proposed in this paper. Initially the genuine and test signatures belonging to an individual are normalized after calculating the aspect ratios of the genuine signatures. The horizontal and vertical projection features of a signature are extracted using discrete Radon transform and the two vectors are combined to form a combined projection feature vector. The feature vectors of two signatures are matched using DTW algorithm. The closed area formed by the matching path around the diagonal of the DTW-grid is computed and is multiplied with the dierence cost between the feature vectors. A threshold is calculated for each genuine sample during the training. The test signature is compared with each genuine sample and a matching score is calculated. A decision to accept or reject is made on the average of such scores. The entire experimentations were performed on a global signature database (GPDS-Signature Database) of 2106 signatures with 936 genuine signatures and 1170 skilled forgeries. To evaluate the performance, experiments were carried out with 4 to 5 genuine samples for training and with dierent ‘scores’. The proposed as well as the existing DTW-method were implemented and compared. It is observed that the proposed method is superior in terms of Equal Error Rate (EER) and Total Error Rate (TER) when 4 or 5 genuine signatures were used for training. Also it is observed that the False Acceptance Rate (FAR) of the proposed system decreases as the number of genuine training samples increases.

Automatic processing of handwritten bank cheque images: a survey

Bank cheques (checks) are still widely used all over the world for financial transactions. Huge volumes of handwritten bank cheques are processed manually every day in developing countries. In such a manual verification, user written information including date, signature, legal and courtesy amounts present on each cheque has to be visually verified. As many countries use cheque truncation systems (CTS) nowadays, much time, effort and money can be saved if this entire process of recognition, verification and data entry is done automatically using images of cheques. An attempt is made in this paper to present the state of the art in automatic processing of handwritten cheque images. It discusses the important results reported so far in preprocessing, extraction, recognition and verification of handwritten fields on bank cheques and highlights the positive directions of research till date. The paper has a comprehensive bibliography of many references as a support for researchers working in the field of automatic bank cheque processing. The paper also contains some information about the products available in the market for automatic cheque processing. To the best of our knowledge, there is no survey in the area of automatic cheque processing, and there is a need of such a survey to know the state of the art.

Survey on Intrusion Detection System using Machine Learning Techniques

In today’s world, almost everybody is affluent with computers and network based technology is growing by leaps and bounds. So, network security has become very important, rather an inevitable part of computer system. An Intrusion Detection System (IDS) is designed to detect system attacks and classify system activities into normal and abnormal form. Machine learning techniques have been applied to intrusion detection systems which have an important role in detecting Intrusions. This paper reviews different machine approaches for Intrusion detection system. This paper also presents the system design of an Intrusion detection system to reduce false alarm rate and improve accuracy to detect intrusion.

Database Development and Recognition of Handwritten Devanagari Legal Amount Words

A dataset containing 26,720 handwritten legal amount words written in Hindi and Marathi languages (Devanagari script) is presented in this paper along with a training-free technique to recognize such handwritten legal amounts present on Indian bank cheques. The recognition of handwritten legal amount words in Hindi and Marathi languages is a challenging because of the similar size and shape of many words in the lexicon. Moreover, many words have same suffixes or prefixes. The recognition technique proposed is a combination of two approaches. The first approach is based on gradient, structural and cavity (GSC) features along with a binary vector matching (BVM) technique. The second approach is based on vertical projection profile (VPP) feature and dynamic time warping (DTW). A number of highly matched words in both the approaches are considered for the recognition step in the combined approach based on a ranking scheme. Syntactical knowledge related to the languages is also used to achieve higher reliability. To the best of our knowledge, this is the first work of its kind in recognizing handwritten legal amounts written in Hindi and Marathi. Researchers interested in the dataset can contact the authors to get it through a shared link.

Face Recognition by PCA Technique

Face recognition is one of the most active research areas in computer vision and pattern recognition with practical applications. This work proposes an apperence based Eigenface technique. PCA is used in extracting the relevant information in human faces. In this method the Eigen vectors of the set of training images are calculated which define the face space. Face images are projected on to the face space which encodes the variation among known face images. These encoded variations are used for recognition. Experiments are carried on IndianFace Database; the obtained recognition rate is 92.30%. The same training set is tested with nonface database.

Effective intrusion detection system using semi-supervised learning

Network security is a very important aspect of internet enabled systems in the present world scenario. As the internet keeps developing the number of security attacks as well as their severity has shown a significant increase. Due to intricate chain of computers the opportunities for intrusions and attacks have increased. Therefore it is need of the hour to find the best ways possible to protect our systems. Every day new kind of attacks are being faced by industries. Hence intrusion detection system are playing vital role for computer security. The most effective method used to solve problem of IDS is machine learning. Getting labeled data does not only require more time but it is also expensive. Labeled data along with unlabeled data is used in semi-supervised methods. The rising field of semi-supervised learning offers a assured way for complementary research. In this paper, an effective semi-supervised method to reduce false alarm rate and to improve detection rate for IDS is proposed.

Machine Learning Using K-Nearest Neighbor for Library Resources Classification in Agent-Based Library Recommender System

Agent-based library recommender system is proposed with the objective to provide effective and intelligent use of library resources such as finding right book(s), relevant research journal papers, and articles. It is composed of profile agent and library recommender agent. Library recommender agent performs the main task of filtering and providing recommendations. Library resources include book records having table of contents and journal articles including abstract and keywords. This provides availability of rich set of keywords to compute similarity. The library resources are classified into fourteen categories specified in ACM computing classification system 2012. The identified category provides a way to obtain semantically related keywords for the library resources. The results of k-Nearest Neighbor (k-NN) for library recommender system are encouraging as there is improvement in the existing results. Use of ACM CCS 2012 as ontology, semantic similarity computation, implicit auto update of user profiles, and variety of users in evaluation are the features of the complete recommender system which makes it useful and novel. This paper details classification of library resources performed by library recommender agent.

Effective semi-supervised approach towards intrusion detection system using machine learning techniques

Network security plays a very important role in todays web enabled world. In the 21st century, network traffic has increased because of the enormous growth in online users and their online communication. The number of security attacks is an increased with increase in internet users. The frequency and severity of such attacks have shown a great impact on network performance. Many classical machine learning algorithms have been proposed to solve the problem of intrusion detection with varying levels of success. Nowadays, availability of libelled data is a big issue. It is not only time consuming, but also expensive. Semi-supervised learning methods can make use of unlabelled examples in addition to the labelled ones. The developing field of semi-supervised learning, offers a promising direction for supplementary research. In this paper, we introduce a new semi-supervised mechanism for intrusion detection, which efficiently reduces false alarms, still maintaining a high detection rate. In our proposed semi-supervised learning approach, only a small quantity of labelled data and a large amount of unlabelled data has been used. This will improve the overall network security by reducing the security administrators efforts and making the alert mechanism more practical.

Measuring similarity between user profile and library book

In the development of recommender system either the content or collaborative filtering is necessary. To filter the records it is required to measure the similarity between profile of user and items present in the dataset. This experiment is performed on the dataset containing 978 books related to computer science field and 7 users. Similarity between profile of user and contents of book is measured using Euclidean, Manhattan, Minkowski, Cosine distances. The results are evaluated and compared. This work is useful in the development of library recommender system.