Offline signature verification paves way to an effective automation of authorizations needed in various real world applications. The use of a neural network in the automatic identification of signatures supports a faster validation that involves lowering labor costs as well as eliminating any form of bias. The models presently in use perform an image embedding based comparison between the test signature’s extracted features and those in the dataset. This often requires the original signee to go through the cumbersome task of producing signatures multiple times, which is not applicable in real world scenarios. The novelty of the proposed work in offline signature verification involves generating a dataset from a very minimalistic number of original signatures. Each of these has its own set of variations produced by augmenting the original image over composite functions. The most similar amongst these alternatives at any given point of time is selected via a greedy approach, thereby reducing the computation required over the Siamese Network. The model was tested on standard datasets as well as those that were locally generated. As a whole, the model is able to classify signatures with an accuracy of 97% and an F1 score of 0.97 on average.

脱机签名验证为有效地自动化各种现实应用中所需的授权铺平了道路。在签名的自动识别中使用神经网络可支持更快的验证，这涉及降低人工成本以及消除任何形式的偏见。当前使用的模型在测试签名的提取特征与数据集中的特征之间执行基于图像嵌入的比较。这通常要求原始签收人完成多次生成签名的繁琐任务，这不适用于现实情况。脱机签名验证中提出的工作的新颖性涉及从极少数量的原始签名中生成数据集。这些中的每一个都有其自己的一组变体，这些变体是通过在复合函数上扩展原始图像而产生的。这些选择之间在任何给定时间点上最相似的选择都是通过贪婪的方法进行的，从而减少了暹罗网络上所需的计算量。该模型在标准数据集以及本地生成的数据集上进行了测试。总体而言，该模型能够以97％的准确度和平均0.97的F1分数对签名进行分类。