Hand-Drawn Emoji Recognition using Convolutional Neural Network

Abstract

Emojis are like small icons or images used to express our sentiments or feelings via text messages. They are extensively used in different social media platforms like Facebook, Twitter, Instagram etc. We considered hand-drawn emojis to classify them into 8 classes in this research paper. Hand-drawn emojis are the emojis drawn in any digital platform or in just a paper with a pen. This paper will enable the users to classify the hand-drawn emojis so that they could use them in any social media without any confusion. We made a local dataset of 500 images for each class summing a total of 4000 images of handdrawn emojis. We presented a system which could recognise and classify the emojis into 8 classes with a convolutional neural network model. The model could favorably recognise as well as classify the hand-drawn emojis with an accuracy of 97%. Some pre-trained CNN models like VGG16, VGG19, ResNet50, MobileNetV2, InceptionV3 and Xception are also trained on the dataset to compare the accuracy and check whether they are better than the proposed one. On the other hand, machine learning models like SVM, Random Forest, Adaboost, Decision Tree and XGboost are also implemented on the dataset.

Emoji’s sentiment score estimation using convolutional neural network with multi-scale emoji images

“…They also suggested that additional improvements in CNN models could be achieved by collecting more data and exploring the use of generative adversarial networks for generating emoticons. Akter et al [30] presented a system for detecting and categorizing hand-drawn emojis into eight distinct classes using a CNN model. A local dataset of 4,000 images was generated and 97% accuracy can be achieved.…”