Design of a Smart Unmanned Ground Vehicle for Hazardous Environments

A smart Unmanned Ground Vehicle (UGV) is designed and developed for some application specific missions to operate predominantly in hazardous environments. In our work, we have developed a small and lightweight vehicle to operate in general cross-country terrains in or without daylight. The UGV can send visual feedbacks to the operator at a remote location. Onboard infrared sensors can detect the obstacles around the UGV and sends signals to the operator.Comment: In proceedings of 2nd National Conference on Recent Trends in Information Systems (ReTIS-08), pp. 222-225, Feb 7-9, 2008, Kolkat

An Automated Group Key Authentication System Using Secret Image Sharing Scheme

In an open network environment, privacy of group communication and integrity of the communication data are the two major issues related to secured information exchange. The required level of security may be achieved by authenticating a group key in the communication channel, where contribution from each group member becomes a part of the overall group key. In the current work, we have developed an authentication system through Central Administrative Server (CAS) for automatic integration and validation of the group key. For secured group communication, the CAS generates a secret alphanumeric group key image. Using secret image sharing scheme, this group key image shares are distributed among all the participating group members in the open network. Some or all the secret shares may be merged to reconstruct the group key image at CAS. A k-nearest neighbor classifier with 48 features to represent the images, is used to validate the reconstructed image with the one stored in the CAS. 48 topological features are used to represent the reconstructed group key image. We have achieved 99.1% classification accuracy for 26 printed English uppercase characters and 10 numeric digits

A novel scheme for binarization of vehicle images using hierarchical histogram equalization technique

Automatic License Plate Recognition system is a challenging area of research now-a-days and binarization is an integral and most important part of it. In case of a real life scenario, most of existing methods fail to properly binarize the image of a vehicle in a congested road, captured through a CCD camera. In the current work we have applied histogram equalization technique over the complete image and also over different hierarchy of image partitioning. A novel scheme is formulated for giving the membership value to each pixel for each hierarchy of histogram equalization. Then the image is binarized depending on the net membership value of each pixel. The technique is exhaustively evaluated on the vehicle image dataset as well as the license plate dataset, giving satisfactory performances.Comment: International Conference on Computer, Communication, Control and Information Technology (C3IT 2009

A Hough Transform based Technique for Text Segmentation

Text segmentation is an inherent part of an OCR system irrespective of the domain of application of it. The OCR system contains a segmentation module where the text lines, words and ultimately the characters must be segmented properly for its successful recognition. The present work implements a Hough transform based technique for line and word segmentation from digitized images. The proposed technique is applied not only on the document image dataset but also on dataset for business card reader system and license plate recognition system. For standardization of the performance of the system the technique is also applied on public domain dataset published in the website by CMATER, Jadavpur University. The document images consist of multi-script printed and hand written text lines with variety in script and line spacing in single document image. The technique performs quite satisfactorily when applied on mobile camera captured business card images with low resolution. The usefulness of the technique is verified by applying it in a commercial project for localization of license plate of vehicles from surveillance camera images by the process of segmentation itself. The accuracy of the technique for word segmentation, as verified experimentally, is 85.7% for document images, 94.6% for business card images and 88% for surveillance camera images

A two-pass fuzzy-geno approach to pattern classification

The work presents an extension of the fuzzy approach to 2-D shape recognition [1] through refinement of initial or coarse classification decisions under a two pass approach. In this approach, an unknown pattern is classified by refining possible classification decisions obtained through coarse classification of the same. To build a fuzzy model of a pattern class horizontal and vertical fuzzy partitions on the sample images of the class are optimized using genetic algorithm. To make coarse classification decisions about an unknown pattern, the fuzzy representation of the pattern is compared with models of all pattern classes through a specially designed similarity measure. Coarse classification decisions are refined in the second pass to obtain the final classification decision of the unknown pattern. To do so, optimized horizontal and vertical fuzzy partitions are again created on certain regions of the image frame, specific to each group of similar type of pattern classes. It is observed through experiments that the technique improves the overall recognition rate from 86.2%, in the first pass, to 90.4% after the second pass, with 500 training samples of handwritten digits

Text/Graphics Separation and Skew Correction of Text Regions of Business Card Images for Mobile Devices

Separation of the text regions from background texture and graphics is an important step of any optical character recognition system for the images containing both texts and graphics. In this paper, we have presented a novel text/graphics separation technique and a method for skew correction of text regions extracted from business card images captured with a cell-phone camera. At first, the background is eliminated at a coarse level based on intensity variance. This makes the foreground components distinct from each other. Then the non-text components are removed using various characteristic features of text and graphics. Finally, the text regions are skew corrected for further processing. Experimenting with business card images of various resolutions, we have found an optimum performance of 98.25% (recall) with 0.75 MP images, that takes 0.17 seconds processing time and 1.1 MB peak memory on a moderately powerful computer (DualCore 1.73 GHz Processor, 1 GB RAM, 1 MB L2 Cache). The developed technique is computationally efficient and consumes low memory so as to be applicable on mobile devices

Segmentation of Offline Handwritten Bengali Script

Character segmentation has long been one of the most critical areas of optical character recognition process. Through this operation, an image of a sequence of characters, which may be connected in some cases, is decomposed into sub-images of individual alphabetic symbols. In this paper, segmentation of cursive handwritten script of world's fourth popular language, Bengali, is considered. Unlike English script, Bengali handwritten characters and its components often encircle the main character, making the conventional segmentation methodologies inapplicable. Experimental results, using the proposed segmentation technique, on sample cursive handwritten data containing 218 ideal segmentation points show a success rate of 97.7%. Further feature-analysis on these segments may lead to actual recognition of handwritten cursive Bengali script.Comment: Proceedings of 28th IEEE ACE, pp. 171-174, December 2002, Science City, Kolkat

Development of a multi-user handwriting recognition system using Tesseract open source OCR engine

The objective of the paper is to recognize handwritten samples of lower case Roman script using Tesseract open source Optical Character Recognition (OCR) engine under Apache License 2.0. Handwritten data samples containing isolated and free-flow text were collected from different users. Tesseract is trained with user-specific data samples of both the categories of document pages to generate separate user-models representing a unique language-set. Each such language-set recognizes isolated and free-flow handwritten test samples collected from the designated user. On a three user model, the system is trained with 1844, 1535 and 1113 isolated handwritten character samples collected from three different users and the performance is tested on 1133, 1186 and 1204 character samples, collected form the test sets of the three users respectively. The user specific character level accuracies were obtained as 87.92%, 81.53% and 65.71% respectively. The overall character-level accuracy of the system is observed as 78.39%. The system fails to segment 10.96% characters and erroneously classifies 10.65% characters on the overall dataset.Comment: Proc. International Conference on C3IT (2009) 240-24

Multicollinearity Correction and Combined Feature Effect in Shapley Values

Model interpretability is one of the most intriguing problems in most of the Machine Learning models, particularly for those that are mathematically sophisticated. Computing Shapley Values are arguably the best approach so far to find the importance of each feature in a model, at the row level. In other words, Shapley values represent the importance of a feature for a particular row, especially for Classification or Regression problems. One of the biggest limitations of Shapley vales is that, Shapley value calculations assume all the features are uncorrelated (independent of each other), this assumption is often incorrect. To address this problem, we present a unified framework to calculate Shapley values with correlated features. To be more specific, we do an adjustment (Matrix formulation) of the features while calculating Independent Shapley values for the rows. Moreover, we have given a Mathematical proof against the said adjustments. With these adjustments, Shapley values (Importance) for the features become independent of the correlations existing between them. We have also enhanced this adjustment concept for more than features. As the Shapley values are additive, to calculate combined effect of two features, we just have to add their individual Shapley values. This is again not right if one or more of the features (used in the combination) are correlated with the other features (not in the combination). We have addressed this problem also by extending the correlation adjustment for one feature to multiple features in the said combination for which Shapley values are determined. Our implementation of this method proves that our method is computationally efficient also, compared to original Shapley method.Comment: 10 page

Online interpretation of numeric sign language using 2-d skeletal model

Gesturing is one of the natural modes of human communication. Signs produced by gestures can have a basic meaning coupled with additional information that is layered over the basic meaning of the sign. Sign language is an important example of communicative gestures that are highly structured and well accepted across the world as a communication medium for deaf and dumb. In this paper, an online recognition scheme is proposed to interpret the standard numeric sign language comprising of 10 basic hand symbols. A web camera is used to capture the real time hand movements as input to the system. The basic meaning of the hand gesture is extracted from the input data frame by analysing the shape of the hand, considering its orientation, movement and location to be fixed. The input hand shape is processed to identify the palm structure, fingertips and their relative positions and the presence of the extended thumb. A 2-dimensional skeletal model is generated from the acquired shape information to represent and subsequently interpret the basic meaning of the hand gesture