A Combined Deep Cnn-Lstm Network For Sketch Recognition

Abstract

Automating freehand sketches is a complex process due to their diverse and abstract characteristics. Recently, there has been significant interest among researchers in machine learning algorithms, owing to their emergence. Nevertheless, many utilized models are either inadequate or overly complex, featuring processes that lack clarity and consistency, which hinders their ability to accurately depict real-world scenarios. In this study, we introduce an approach that applies deep learning methods involving a combination of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) to enhance sketch recognition performance. In the initial phase of our approach, a CNN was employed to extract features that were subsequently forwarded to an LSTM network for classification. We evaluated the efficacy of our method by utilizing the QuickDraw dataset offered by Google, and the results demonstrated that our approach outperformed both CNN and LSTM, as well as other state-of-the-art methods. Our method attained an accuracy of 95%, with precision and recall reaching 95%, while also achieving an F1 score of 94%.