A Comparative Study of Deep Learning Models for Doodle Recognition

Abstract

Recent advancements in the field of Deep Neural Networks have led researchers and industries to challenge the boundaries between humans and machines. Computers are advancing at great speed in this age. If they have the ability to understand our doodles or quick line drawings, it will allow for much more advanced and simplified forms of communication. This has a major effect on handwriting recognition and its important applications in the areas of OCR (Optical Character Recognition), ASR (Automatic Speech Recognition) and NLP (Nature Language Processing). This paper aims to implement various techniques of deep learning to efficiently recognize labels of hand-drawn doodles. In this study, the Google Quick Draw dataset has been used to train and evaluate the models. Various state-of- the-art transfer learning approaches are compared, and the traditional Convolutional Neural Network approach is also examined. Two pre-trained models - VGG16, and MobileNet are used. We have compared these models using various metrics such as top 3 accuracy percentage on training and validation data and also Mean Average Precision (mAP@3). The result shows that the Transfer Learning model with VGG16 architecture outperforms the other methods, giving an accuracy of 93.97% on 340 categories.