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In this work, an offline signature identification system based on Histogram of Oriented Gradients (HOG) vector features is designed. Handwritten signature images are collected at Yildiz Technical University, from 15 people, 40 samples from each. Before the HOG feature extraction, size fixing and noise reduction processes are applied to all signature images. HOG features are extracted from the noiseless...
In this paper we are going to apply four descriptors (GIST, PHOG, SURF and Centrist) and two classifiers (Artificial Neural Network (ANN) and Support Vector Machines (SVM)) for handwritten mathematical symbols recognition to achieve a comparative study based on the recognition rate.
In this paper a novel approach for recognition of handwritten digits for South Indian languages using artificial neural networks (ANN) and Histogram of Oriented Gradients (HOG) features is presented. The images of documents containing the hand written digits are optically scanned and are segmented into individual images of isolated digits. HOG features are then extracted from these images and applied...
This paper proposes a technique for automatic recognition of Bengali handwritten numerals using multiple feature sets. We discuss about some novel Morphological features and k-curvature feature extraction technique to recognize handwritten scripts. We use different multi-layer perceptron (MLP) classifiers to train this feature spaces and then fuse those classifiers using modified `Naive'-Bayes combination...
Feature extraction is an important process in off-line signature verification. In this work, the performance of two feature extraction techniques, the Modified Direction Feature (MDF) and the gradient feature are compared on the basis of similar experimental settings. In addition, the performance of Support Vector Machines (SVMs) and the squared Mahalanobis distance classifier employing the Gradient...
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