An inference engine for classification of Electrocardiogram (ECG) signals is developed with the help of a rule based rough set decision system. For this purpose an automated data extraction system from ECG strips is b...
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Extraction of skeletal shape from a 2D dot pattern is discussed. We use a self-organizing neural network model to get a piecewise linear approximation of a skeleton of the pattern. It is found that even without a prop...
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The goal of this project is to build a Contextual Suggestion system that will recommend the usel a lanked list of suggestions depending on user's context as well as her preferences. In this context we prent an alg...
Date is a useful information for various application (e.g. date wise document indexing) and automatic extraction of date information involves difficult challenges due to writing styles of different individuals, touchi...
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Signatures continue to be an important biometric because they remain widely used as a means of personal verification and therefore an automatic verification system is needed. Manual signature-based authentication of a...
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Signatures continue to be an important biometric because they remain widely used as a means of personal verification and therefore an automatic verification system is needed. Manual signature-based authentication of a large number of documents is a difficult and time consuming task. Consequently for many years, in the field of protected communication and financial applications, we have observed an explosive growth in biometric personal authentication systems that are closely connected with measurable unique physical characteristics (e.g. hand geometry, iris scan, finger prints or DNA) or behavioural features. Substantial research has been undertaken in the field of signature verification involving English signatures, but to the best of our knowledge, very few works have considered non-English signatures such as Chinese, Japanese, Arabic etc. In order to convey the state-of-the-art in the field to researchers, in this paper we present a survey of non-English and non-Latin signature verification systems.
There are many video images where hand written text may appear. Therefore handwritten scene text detection in video is essential and useful for many applications for efficient indexing, retrieval etc. Also there are m...
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The Connected TV can be described as an Internet enabled TV. In the current paper we have proposed a system for connected TV that mash up the information from internet and RSS feeds related to the breaking news aired ...
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Signature identification and verification are of great importance in authentication systems. The purpose of this paper is to introduce an experimental contribution in the direction of multi-script off-line signature i...
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Signature identification and verification are of great importance in authentication systems. The purpose of this paper is to introduce an experimental contribution in the direction of multi-script off-line signature identification and verification using a novel technique involving off-line English, Hindi (Devnagari) and Bangla (Bengali) signatures. In the first evaluation stage of the proposed signature verification technique, the performance of a multi-script off-line signature verification system, considering a joint dataset of English, Hindi and Bangla signatures, was investigated. In the second stage of experimentation, multi-script signatures were identified based on the script type, and subsequently the verification task was explored separately for English, Hindi and Bangla signatures based on the identified script result. The gradient and chain code features were employed, and Support Vector Machines (SVMs) along with the Modified Quadratic Discriminate Function (MQDF) were considered in this scheme. From the experimental results achieved, it is noted that the verification accuracy obtained in the second stage of experiments (where a signature script identification method was introduced) is better than the verification accuracy produced following the first stage of experiments. Experimental results indicated that an average error rate of 20.80% and 16.40% were obtained for two different types of verification experiments.
The primary objective of the model applied in this work is to predict the weather of a city named Austin in Texas using supervised machine learning algorithms. In this case, artificial neural networks and gradient boo...
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Segmentation of handwritten text into lines, words and characters is one of the important steps in the handwritten recognition system. For the segmentation of unconstrained Oriya handwritten text into individual chara...
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Segmentation of handwritten text into lines, words and characters is one of the important steps in the handwritten recognition system. For the segmentation of unconstrained Oriya handwritten text into individual characters, a water reservoir-concept based scheme is proposed in this paper. Here, at first, the text image is segmented into lines, and then lines are segmented into individual words, and words are segmented into individual characters. For line segmentation the document is divided into vertical stripes. Analyzing the heights of the water reservoirs obtained from different components of the document, the width of a stripe is calculated. Stripe-wise horizontal histograms are then computed and the relationship of the peak-valley points of the histograms is used for line segment. Based on vertical projection profile and structural features of Oriya characters, text lines are segmented into words. For character segmentation, at first, isolated and connected (touching) characters in a word are detected. Using structural, topological and water-reservoir-concept based features touching characters of the word are then segmented.
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