Offline Hindi Signature Recognition Using Surf Feature Extraction and Neural Networks Approach
نویسندگان
چکیده
The signatures are one of the ways to identify the signer. Signature recognition is the process of verifying the person’s identity by checking their signature with the signatures which are stored in the database. This process is of two types: offline and online. This paper deals with the offline technique. This technique recognizes the person whether he/she is genuine or forged. In this paper the offline signature recognition technique is proposed using neural networks and surf feature extraction. The signatures are taken as an image form, which are captured by any camera or digital scanner. The parameter are extracted with the help of surf feature extraction method is proposed. The feature extraction is the key to develop the offline signature recognition system. The proposed code is implemented on the Matlab software.
منابع مشابه
Offline Signature Verification Using Surf Feature Extraction and Neural Networks Approach
In this paper we will evaluate the use of SURF features in handwritten signature verification. For each known writer we will take a sample of three genuine signatures and extract their SURF descriptors. In this paper, off-line signature recognition & verification using neural network is proposed, where the signature is captured and presented to the user in an image format. Signatures are verifi...
متن کاملAdaptive SIFT/SURF Algorithm for Off-line signature Recognition
Signature recognition is the process of verifying a writer’s identity by checking the signature against samples previously stored in the database. Several techniques such as the distance-based and statistical classifiers used for feature extraction on a signature image are not invariant to scaling and rotation and the Scale invariant feature transform (SIFT) though invariant to scaling and rota...
متن کاملRecognition of Handwritten Hindi Characters using Backpropagation Neural Network
Automatic recognition of handwritten characters is a difficult task because characters are written in various curved & cursive ways, so they could be of different sizes, orientation, thickness, format and dimension. An offline handwritten Hindi character recognition system using neural network is presented in this paper. Neural networks are good at recognizing handwritten characters as these ne...
متن کاملRecognition Offline Handwritten Hindi Digits Using Multilayer Perceptron Neural Networks
Handwritten Hindi digit recognition plays an important role in eastern Arab countries especially in the courtesy amounts of Arab bank checks. In this paper, we proposed an efficient offline handwritten Hindi digits recognition system and developed using Multilayer Perceptron Neural Network (MLP). The implemented system recognizes separated handwritten Hindi digits scanned using a scanner. The s...
متن کاملNeural Network Based Offline Signature Recognition and Verification System
Handwritten signatures are the most natural way of authenticating a person’s identity. An offline signature verification system generally consists of four components: data acquisition, preprocessing, feature extraction, recognition and verification. This paper presents a method for verifying handwritten signature by using NN architecture. In proposed methods the multi-layer perceptron (MLP), mo...
متن کامل