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Hindi and English Off-line Signature Identification and Verification

机译:印地语和英语离线签名识别和验证

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Biometric systems play a significant role in the field of information security as they are extremely required for user authentication. Signature identification and verification have a great importance for authentication intention. The purpose of this paper is to present an empirical contribution towards the understanding of multi-script (Hindi and English) signature verification. This system will identify whether a claimed signature belongs to the group of English signatures or Hindi signatures from a combined Hindi and English signature datasets and then it will verify signatures using these two resultant signature datasets (Hindi script signature and English script signatures) separately. The modified gradient feature and SVM classifier were employed for identification and verification purposes. To the best of authors' knowledge, the multi-script signature identification and verification has never been used for the task of signature verification and this is the first report of using Hindi and English signatures in this area. Two different results for identification and verification are calculated and analysed. The accuracy of 98.05% is obtained for the identification of signature script using 2160 (1080 Hindi + 1080 English) samples for training and 1080 (540 Hindi + 540 English) samples for testing. The resultant data sets obtained in script identification of signatures were used for verification purpose. The FRR, FAR for Hindi and English was obtained 8.0%, 4.0% and 12.0%, 10.0% respectively.
机译:生物识别系统在信息安全领域发挥着重要作用,因为它们是用户身份验证的非常需要的。签名识别和验证对于认证意图具有重要意义。本文的目的是为了解多脚本(印地文和英语)签名验证的理性贡献。该系统将识别所要求保护的签名属于来自组合的印地文和英文签名数据集的英文签名或印度签名组,然后它将验证使用这两个结果签名数据集(HINDI脚本签名和英语脚本签名)的签名。改进的梯度特征和SVM分类器用于识别和验证目的。据作者所知,多脚本签名识别和验证从未用于签名验证的任务,这是在该区域中使用印地文和英语签名的第一个报告。计算并分析了两种不同的识别和验证结果。获得98.05%的准确性,用于使用2160(1080 Hindi + 1080英语)样本进行培训和1080(540 Hindi + 540英语)样本进行测试的签名脚本。在脚本识别中获得的结果数据集用于验证目的。 FRR是印地语和英语的FRR获得8.0%,4.0%和12.0%,分别为10.0%。

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