Signature Detection and Identification Algorithm with CNN, Numpy and OpenCV

Abstract

The purpose of the article is to present a simple signature detection algorithm and its subsequent signature identification using a deep learning model for processing images based on a convolutional neural network. To solve the task of the image recognition, a binary classification has been performed to predict text or signature and signature classifications to determine the author of this signature. The proposed algorithm is interesting in the preliminary processing of scanned documents with signatures in order to extract the area with the signature and transfer it to the trained model. The research results are presented for documents of the same type, in which the signature is located in the same place. To select a specific element in the document we are using the tensor-slicing operations on Numpy arrays. To extract areas with text and signature, OpenCV tools are used. The results on the ready-made neural network model studies on a small dataset are presented in this article. Good results have been achieved in recognizing the famous writers’ signatures. The proposed algorithm demonstrates the possibility of using the classical convolution network model for solving specific practical problems. The studies can be recommended to students in the study of neural networks to understand the basics of deep learning and apply a ready-made model as a template for solving practical problems in the field of computer vision.