Oral Presentation , Page 199-204 (6)

Volume Title: 2nd IEEE International Conference on Electronic Eng., Faculty of Electronic Eng., Menouf, Egypt, 3-4 July. 2021

¹Ahmed Hagag ; ²Ahmed N. K. Alfarra; ³Fahd Mekawy

¹Department of Scientific Computing, Faculty of Computers and Artificial Intelligence, Benha University, Benha, 13518, Egypt

²School of Management Harbin Institute of Technology Harbin, 150001, China

³Department of Scientific Computing Faculty of Computers and Artificial Intelligence Benha University Benha, 13518, Egypt

With the spread of the COVID19 pandemic, blended learning has become one of the most used methods in educational organizations such as universities, community colleges, and schools. In blended learning, the students’ practical activities are done in more than one way, including simulation software and the place of study. For chemical experiment programs, the classification of handwritten chemical formulas plays an important role in determining the simulation software's efficiency. Accordingly, in this study, we propose a model for handwritten chemical formula classification. First, this paper describes a handwritten chemical formulas dataset that contains eight classes (HCFD8). Second, convolutional neural networks (CNNs) with pre-trained weights are used as a deep feature extractor to extract features from the images. Third, due to limited training images per class, the proposed model uses data augmentation techniques to expand the training images. Then, an enhanced multilayer perceptron (EMLP) strategy is used to classify the image. Finally, we provide a performance analysis of typical deep learning approaches on HCFD8, which shows that the proposed model performs good accuracy results.

Convolutional Neural Network; Transfer learning; Deep learning; Handwritten chemical formulas classification; Data Augmentation