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Greater Noida, India

Ragha L.,RAIT | Sasikumar M.,CDAC
ICMLC 2010 - The 2nd International Conference on Machine Learning and Computing | Year: 2010

The Handwriting character recognition (HCR) for Indian Languages is an important problem where there is relatively little work has been done. In this paper, we investigate the use of moments features on Kannada Kagunita. Kannada characters are curved in nature with some kind of symmetric structure observed in the shape. This information can be best extracted as a feature if we extract moment features from the directional images. To recognize a Kagunita, we need to identify the vowel and the consonant present in the image. So we are finding 4 directional images using Gabor wavelets from the dynamically preprocessed original image. We analyze the Kagunita set and identify the regions with vowel information and consonant information and cut these portions from the preprocessed original image and form a set of cut images. We then extract moments features from them. These features are trained and tested for both vowel and Kagunita recognition on Multi Layer Perceptron with Back Propagation Neural Network. The recognition results for vowels is average 85% and consonants is 59% when tested on separate test data with moments features from directional images and cut images. © 2010 IEEE. Source

Maurya R.,CDAC
ICIIP 2011 - Proceedings: 2011 International Conference on Image Information Processing | Year: 2011

In this paper we proposed the method for road extraction. The road extraction involves the two main steps: the detection of road that might have the other non road parts like buildings and parking lots followed by morphological operations to remove the non road parts based on their features. We used the K-Means clustering to detect the road area and may be some non road area. Morphological operations are used to remove the non road area based on the assumptions that road regions are an elongated area that has largest connected component. © 2011 IEEE. Source

Chari K.S.,Semiconductor Integrated Circuits Layout Design Registry | Sharma M.,CDAC
2011 - International Conference on Signal Processing, Communication, Computing and Networking Technologies, ICSCCN-2011 | Year: 2011

ICs have become pervasive in practically all electronic applications and products. Continuous innovation and tailoring design techniques have spawned several IC designs catering to distinct uses. The paper highlights the matters of comparison of Integrated Circuit (IC) Layout Designs (LDs) using industry standard and custom Electronic Computer Aided Design (ECAD) tools and reports their performance viz -a- viz some key attributes for IC layout design comparison. The features of the tools for Cadence-Virtuoso; Mentor-Caliber; Synopsis-Hercules; Tanner-LEdit, and the two customized tools Softjin-NxCompare and ICLDDTv1 are described along with detailed IC layout comparison example performance runs with Softjin tools. An assessment on catching potential copying or infringements between given IC designs was checked through appropriate GDSII files of the design. From the analysis of the various features and results reported in this paper, it is concluded that the standard IC Design tools lack in their efficiency in terms of layout geometric comparisons and the customized tools demonstrate superior performances proving their immense value for robust comparison of any given Integrated Circuit Layout Design geometric patterns i.e. gds files. The later tools by virtue of their superior functional attributes and analysis abilities could cater to determination of distinctiveness of IC LD patterns as well as absence or extent of copying inherent between two IC LD files (a new LD filed and a gold reference LD file in data base) for Intellectual Property (IP) determinations. The later tools could also aid the IC Designer in the enhancing the innovation process and tagging the third party IPs mapped in to the design. © 2011 IEEE. Source

Pirani Z.,M.H.S.S.C.O.E | Sasikumar M.,CDAC
Procedia Computer Science | Year: 2015

Learning Disabilities (LD) are usually hidden disabilities that affect many individuals who usually have average or above average intelligence. It is acquired before, during or soon after birth and affects an individual's ability to learn, all through his/her life. LD may also involve difficulties with organizational skills and social interaction. These difficulties can be alleviated by providing appropriate e-learning environment for them. We had proposed a framework, an Assistive Learning Environment (ALE) to enhance the learning experience of LD students in their academic life1, which is capable for recognizing what content has to delivered, variability associated with each LD learner and transformations associated with the content to deliver it to the LD learner. The system architecture is developed for our framework whose objective is to transform the given content in a way acceptable by the specific LD learner. This transformation is a complex process and it has to be done at various levels. Assistive E-Learning System, a prototype implementation of our framework has been completed and sample interactions are presented in order to assess the system's strengths and weakness. The system provides the user to indicate transformations and configurations not appropriate to the user. © 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license. Source

Patnaik T.,CDAC
Communications in Computer and Information Science | Year: 2011

The project 'Development of Robust document Analysis and Recognition for printed Indian Scripts' is a Department of Information Technology sponsored project to develop OCR for printed Indian scripts. A consortia led by IIT Delhi has completed the phase -I in OCR. The consortia members include 1 IIT Delhi 2 IISC Bangalore 3 ISI Kolkatta 4 IIIT Hyderabad 5 Central University, Hyderabad 6 Punjabi University, Patiala 7 MS University, Baroda 8 Utkal University, Bhubaneswar 9 CDAC Noida 10 CDAC Pune Different consortia members are responsible for different language OCRs like Punjabi University has contributed. Gurumukhi OCR, IIIT Hyderabad for Malalayam OCR etc. CDAC Noida has done the integration of OCRs with pre processings. © 2011 Springer-Verlag. Source

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