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Featured researches published by Sumaira Kausar.


frontiers of information technology | 2011

A Survey on Sign Language Recognition

Sumaira Kausar; M. Younus Javed

Sign Language (SL) recognition is getting more and more attention of the researchers due to its widespread applicability in many fields. This paper is based on the survey of the current research trends in the field of SL recognition to highlight the current status of different research aspects of the area. Paper also critically analyzed the current research to identify the problem areas and challenges faced by the researchers. This identification is aimed at providing guideline for the future advances in the field.


international conference on emerging technologies | 2010

Guidelines for the selection of elicitation techniques

Sumaira Kausar; Saima Tariq; Saba Riaz; Aasia Khanum

Requirement Elicitation is a crucial part of Requirement Engineering. Using an appropriate method can help in producing a consistent and complete set of requirements with reduced cost and time. This paper introduces ways and guidelines for selecting requirement elicitation techniques. Different elicitation techniques are analyzed in the light of different project settings. Guidelines for selecting an appropriate mix of elicitation techniques are presented to overcome individual shortcomings of the techniques and maximize the aggregate advantage.


International Journal of Pattern Recognition and Artificial Intelligence | 2015

A Caption Text Detection Method from Images/Videos for Efficient Indexing and Retrieval of Multimedia Data

Samabia Tehsin; Asif Masood; Sumaira Kausar; Yunous Javed

Textual information embedded in multimedia can provide a vital tool for indexing and retrieval. Text extraction process has many inherent problems due to the variation in font sizes, color, backgrounds and resolution. Text detection and localization are the most challenging phases of text extraction process whereas text extraction results are highly dependent upon these phases. This paper focuses on the text localization because of its very fundamental importance. Two effective feature vectors are introduced for the classification of the text and nontext objects. First feature vector is represented by the Radon transform of text candidate objects. Second feature vector is derived from the detailed geometrical analysis of text contents. Union of two feature vectors is used for the classification of text and nontext objects using support vector machine (SVM). Text detection and localization results are evaluated on two publicly available datasets namely ICDAR 2013 and IPC-Artificial text. Moreover, results are compared with state-of-the-art techniques and the Comparison demonstrates the superiority of the presented research.


Iete Journal of Research | 2013

Text Localization and Detection Method for Born-digital Images

Samabia Tehsin; Asif Masood; Sumaira Kausar; Younus Javed

Abstract Multimedia data has increased rapidly in recent years. Textual information present in multimedia contains important information about the image/video content. The proposed method provides very efficient way to extract text from Born-Digital images. Firstly, edges are extracted from a grayscale image. New edge detection technique is introduced in this research, which gives better results for low-contrast web images. Then morphological operators are applied on the image. These operators are used to connect the broken edges of objects present in an image. Each object is classified as text or non-text on the basis of text features such as size, height to width ratio, and binary transitions, using K-Means clustering. Two new features, namely horizontal fluctuation count and vertical fluctuation count, are introduced in the proposed work. Dataset of International Conference on Document Analysis and Recognition 2011 Robust Reading Competition, Challenge 1: “Reading Text in Born-Digital Images (Web and Email)” is used in this research. The proposed method performed best in the above-mentioned competition.


International Journal of Pattern Recognition and Artificial Intelligence | 2016

A Novel Mathematical Modeling and Parameterization for Sign Language Classification

Sumaira Kausar; M. Younus Javed; Samabia Tehsin; Almas Anjum

Sign language recognition (SLR) has got wide applicability. SLR system is considered to be a challenging one. This paper presents empirical analysis of different mathematical models for Pakistan SLR (PSLR). The proposed method is using the parameterization of sign signature. Each sign is represented with a mathematical function and then coefficients of these functions are used as the feature vector. This approach is based on exhaustive experimentation and analysis for getting the best suitable mathematical representation for each sign. This extensive empirical analysis, results in a very small feature vector and hence to a very efficient system. The robust proposed method has got general applicability as it just need a new training set and it can work equally good for any other dataset. Sign set used is quite complex in the sense that intersign similarity distance is very small but even then proposed methodology has given quite promising results.


Mathematical Problems in Engineering | 2014

Fuzzy-Based Segmentation for Variable Font-Sized Text Extraction from Images/Videos

Samabia Tehsin; Asif Masood; Sumaira Kausar; Fahim Arif

Textual information embedded in multimedia can provide a vital tool for indexing and retrieval. A lot of work is done in the field of text localization and detection because of its very fundamental importance. One of the biggest challenges of text detection is to deal with variation in font sizes and image resolution. This problem gets elevated due to the undersegmentation or oversegmentation of the regions in an image. The paper addresses this problem by proposing a solution using novel fuzzy-based method. This paper advocates postprocessing segmentation method that can solve the problem of variation in text sizes and image resolution. The methodology is tested on ICDAR 2011 Robust Reading Challenge dataset which amply proves the strength of the recommended method.


ISCGAV'08 Proceedings of the 8th conference on Signal processing, computational geometry and artificial vision | 2008

Recognition of gestures in Pakistani sign language using fuzzy classifier

Sumaira Kausar; M. Younus Javed; Shaleeza Sohail


International Journal of Image, Graphics and Signal Processing | 2014

Survey of Region-Based Text Extraction Techniques for Efficient Indexing of Image/Video Retrieval

Samabia Tehsin; Asif Masood; Sumaira Kausar


International Journal of Engineering | 2017

Anatomical Survey Based Feature Vector for Text Pattern Detection

Samabia Tehsin; Sumaira Kausar


International Journal of Image, Graphics and Signal Processing | 2016

Vision-based Classification of Pakistani Sign Language

Sumaira Kausar; M. Younus Javed; Samabia Tehsin; Muhammad Riaz

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Samabia Tehsin

National University of Science and Technology

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M. Younus Javed

College of Electrical and Mechanical Engineering

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Asif Masood

National University of Science and Technology

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Aasia Khanum

National University of Sciences and Technology

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Saba Riaz

National University of Sciences and Technology

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Saima Tariq

National University of Sciences and Technology

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Shaleeza Sohail

College of Electrical and Mechanical Engineering

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Younus Javed

National University of Science and Technology

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