OPTIMIZATION OF CONVOLUTIONAL NEURAL NETWORK ALGORITHMIC ACCURACY FOR THE IDENTIFICATION OF DIFFERENT FONT TYPES - Kanata, Bulkis; Misbahuddin; Akhdan, M. Rafif (Jurnal Teknik Informatika C.I.T Medicom, 2025)
Text not only conveys the message through the words used, but also through its visual aspects. One of the most influential visual elements is the type of font. Recognising and determining font types appropriately is essential, whether in the academic sector, the printing industry, graphic design, or digital systems. However, in practice, manually recognising font types takes time, skill, and high precision. With the advancement of digital technology, the variety of font types is increasing, making the process of identifying fonts more complicated. This requires the development of methods that are able to distinguish different types of fonts precisely and accurately. This study reveals the potential of Convolutional Neural Network (CNN) algorithms as an optimisation in facing font identification challenges, as well as to prove that deep learning can provide more efficient and precise solutions, by comparing three different CNN architectures, namely DenseNet121, ResNet50, and VGG16. The implementation of the method is carried out by applying data augmentation techniques and setting CNN parameters such as the number of epochs, learning rate, batch size, Adam optimiser, and image size. The results showed that the DenseNet121 model achieved an accuracy of up to 96.8%, ResNet50 92.9%, and VGG16 96.4%. The convolutional neural network algorithm proves that it can identify various font types with optimal accuracy.