Result: RESEARCH ON SIGNATURE RECOGNITION METHOD BASED ON DEEP LEARNING TECHNIQUE IN PYTHON LANGUAGE.

Expression recognition often relies on a CNN for extraction of important features from image data before that image data can be used by the RNN. Similarly, signature recognition is an important problem in the field of security and identity authentication. With the development of deep learning, models such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) have been widely applied to automate the process of classifying real and fake signatures. This paper presents a comprehensive study on the construction of a signature recognition system using deep learning with Python. We focus on the preprocessing of signature images, the construction of a CNN model architecture combined with RNN to exploit image features and stroke sequences, and the performance comparison between models. Through experiments on diverse datasets, the system achieves an accuracy of up to 96% in distinguishing "real signatures" and "forged signatures". In addition, the paper also analyzes the influencing factors, existing limitations, and proposes directions for future research. [ABSTRACT FROM AUTHOR]