Review on Signature Recognition using Neural Network, SVM, Classifier Combination of HOG and LBP features

Sangeeta; Er. Manpreet Kaur

CALL FOR PAPERS : Feb-2026

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25-Feb-26

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Review on Signature Recognition using Neural Network, SVM, Classifier Combination of HOG and LBP features

Author(s):

Sangeeta , CGL ,Landra, Mohali; Er. Manpreet Kaur, CEC, Landra, Mohali

Keywords:

Signature verification, Neural Network, HOG, LBP and SVM

Abstract:

Signature verification systems can be categorized as offline (static) and online (dynamic). This paper presents comparison between neural network, SVM and Classifier Combination of HOG and LBP features with surf feature based recognition of offline signatures system that is trained with poor-resolution scanned signature images. The signature of a person is an important biometric attribute of a human being which can be used to authenticate person’s identity. However signatures can be taken asan image and recognized using computer vision and neural network and SVM with surf feature methods. With high speed computers, there is need to develop fast and robust algorithms for signature recognition. There are various approaches to signature recognition with a lot of scope of research. The Off-line Signature Recognition and verification is implemented using Matlab where the Neural Network is trained using all the attributes of a given image. For the implementation of this work Matlab software will be used. Whereas another approach follows the process of extracting out information from the image and creating a Histogram (HOG) using the vectors. After extracting, data is classified using Support Vector Machine (SVM).