Hand Printed Character Recognition Using Neural Networks

In this paper an attempt is made to recognize hand-printed characters by using features extracted using the proposed sector approach. In this approach, the normalized and thinned character image is divided into sectors with each sector covering a fixed angle. The features totaling 32 include vector distances, angles, occupancy and end-points. For recognition, both neural networks and fuzzy logic techniques are adopted. The proposed approach is implemented and tested on hand-printed isolated character database consisting of English characters, digits and some of the keyboard special characters

Automatic Segmentation and Recognition of Bank Cheque Fields

This paper describes a novel method for automatically segmenting and recognizing the various information fields present on a bank cheque. The uniqueness of our approach lies in the fact that it doesn't necessitate any prior information and requires minimum human intervention. The extraction of segmented fields is accomplished by means of a connectivity based approach. For the recognition part, we have proposed four innovative features, namely; entropy, energy, aspect ratio and average fuzzy membership values. Though no particular feature is pertinent in itself but a combination of these is used for differentiating between the fields. Finally, a fuzzy neural network is trained to identify the desired fields. The system performance is quite promising on a large dataset of real and synthetic cheque images

Automatic Handwritten Signature Verification System for Australian Passports

We present an automatic handwritten signature verification system to prevent identity fraud by verifying the authenticity of signatures on Australian passports. In this work, fuzzy modeling has been employed for developing a robust recognition system. The knowledge base consists of unique angle features extracted using the box method. These features are fuzzified by an exponential membership function, consisting of two structural parameters which have been devised to track even the minutest variations in a person's signature. The membership functions in turn constitute the weights in the Takagi-Sugeno (TS) model. The optimization of the output of the TS model with respect to the structural parameters yields the solution for the parameters. The efficacy of the proposed system has been tested on a large database of over 1200 signature images obtained from 40 volunteers achieving a recognition rate of more than 99%

Area Based Novel Approach for Fuzzy Edge Detection

This paper presents a novel approach for edge detection based on the univalue segment assimilating nucleus (USAN) area. The USAN area characterizes the structure of the edge present in the neighborhood of a pixel and can thus be considered as a unique feature of the pixel and is fuzzified. The Gaussian edge detector mask is then applied to the associated Gaussian membership function of the USAN area. A threshold is applied on the resultant gradient image to yield the binary image. The results of the proposed edge detector are compared with other well known edge detection techniques and it is found that the image obtained using the proposed approach is qualitatively the best

Blotch detection in pigmented skin lesions using fuzzy co-clustering and texture segmentation

The `fuzzy co-clustering algorithm for images (FCCI)' technique has been successfully applied to colour segmentation of medical images. The goal of this work is to extend this technique by the inclusion of texture features as a clustering parameter for detecting blotches in skin lesions based on colour information. The objective function is optimized using the bacterial foraging algorithm which gives image specific values to the parameters involved in the algorithm. Experiments show the efficacy of the proposed method in extracting malignant blotches from different types of pigmented skin lesion images

Fusion of hand based biometrics using particle swarm optimization

Multi-modal biometrics has numerous advantages over unimodal\ud biometric systems. Decision level fusion is the most\ud popular fusion strategy in multimodal biometric systems. Recent\ud research has shown promising performance of hand based\ud biometrics, i.e. palmprint and hand geometry over other\ud biometric modalities. However, the improvement in\ud performance is constrained by the lack of optimal sensor points\ud and fusion strategy. In this paper, we have implemented a\ud particle swarm based optimization technique for selecting\ud optimal parameters through decision level fusion of two\ud modalities: palmprint and hand geometry. The experimental\ud evaluation on a database of 100 users confirms the utility of the\ud decision level fusion using particle swarm optimization

A palm print authentication system using quantized phase feature representation

This paper presents a new low cost contactless acquisition system for palm print authentication which reduces the deformations caused due to glass plates in sensors and allows the user to place the hand freely in an unconstrained fashion. Band Limited Phase Only Correlation (BLPOC) is utilized to compare two palm images. In the conventional approach using BPLOC, regions of interest (ROIs) are extracted from palm print images and stored in a database for comparison with test images; requiring more storage space as well as computation time. Therefore, instead of storing the ROI for comparison, we stored its phase angle coefficients by quantizing the image into an integer depending on the number of quantization levels used. Although, more quantization levels lead to a more accurate representation of phase angles, they come at a cost of increased feature space. The experimental results implemented on a publicly available database and on an acquired database of 175 users are very promising thus validating this new approach for palm print authentication

Mixed noise correction in gray images using fuzzy filters

This paper presents Gaussian and Impulse noise filters for eliminating mixed noise in images. For Gaussian filter, the fuzzy set called "small" is derived to represent the disorder in a pixel arising out of neighborhood corrupted with Gaussian. The expression for correction is developed based on the intensity of the central pixel and the membership function. Similarly, the correction for the Impulse noise is developed by finding the middle ranking pixels in the neighborhood of the central pixel. The difference between the average of the middle ranking pixels and the central pixels is used to evaluate the membership function which when multiplied by the difference gives the correction. Consequently, the presence of noise is detected by finding the aggregate of the four highest memberships of the neighborhood pixels. If this aggregate is more than the threshold then there is Gaussian noise otherwise impulse noise. Accordingly, the corrupted pixel will be corrected by the correction term. The results are found to be satisfactory

a face recognition approach using zernike moments for video surveillance

In this paper, a face recognition approach using Zernike moments is presented for the main purpose of detecting faces in surveillance cameras. Zernike moments are invariant to rotation and scale and these properties make them an appropriate feature for automatic face recognition. A Viola-Jones detector based on the Adaboost algorithm is employed for detecting the face within an image sequence. Preprocessing is carried out wherever it is needed. A fuzzy enhancement algorithm is also applied to achieve uniform illumination. Zernike moments are then computed from each detected facial image. The final classification is achieved using a kNN classifier. The performance of the proposed methodology is compared on three different benchmark datasets. The results illustrate the efficacy of Zernike moments for the face recognition problem in video surveillance