Offline Signature Recognition Using PCA-FFNN Method and Adaptive Variance Reduction and Invariant Moment Feature Extraction
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2017, Vol 5, Issue 6
Abstract
Although offline handwritten signature recognition has been frequently researched, it nevertheless calls for an improvement of popularity rate. Most of present techniques attention on feature extraction (FE) to improve their performance. In this analyze, we put in force Offline Signature attention utilizing principal component analysis (PCA) and Feed Forward Neural network (FFNN) method. We extract signature features using histogram of Orientation (HOG) and Seven Invariant Moments. The proposed system works in three parts. First Pre-processing: where resizing, binarization, noise reduction is done to make signatures all set for FE consequently, the variance discount technique is utilized to normalize offline handwritten signatures in means of an adaptive dilation operator. Then the range of signatures is analyzed in conditions of coefficient of variant (CV). The optimal CV is obtained and used to be a threshold limit value for the acceptable variance reduction. Within the experimental outcome, we extended signature recognition accuracy in conditions of attention expense up to 95.8%with database of SigWiComp2011 (48 signatures of 12 persons).
Authors and Affiliations
Priyanka Chauhan, Nirupma Tiwari
Keywords
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