Implementation of transfer learning in convolutional neural network architecture for android-based handwriting quality detection

Handwriting is a number, letter, word, or sentence written on a piece of paper that comes from human or individual handwriting. Handwriting has benefits that can help fine motor coordination, memory, and cognitive development in multi-sensory activities. There are several problems in the world of education that involve handwriting. First, handwriting that is difficult to read can cause students to get low grades and even fail to graduate from school. Second, sloppy handwriting makes students lose confidence. Third, students who have bad handwriting have the potential to be bullied by friends at school. The description of these problems causes the author to be interested in developing a digital technology innovation in detecting the quality of handwriting. The purpose of this research is to detect handwriting quality using a Convolutional Neural Network in transfer learning process of EfficientNet B0 model architecture on fine tuning. The data used are secondary data from the CSAFE Handwriting Database at Iowa State University collected by researchers at the Center for Statistics and Applications in Forensic Evidence. This data consists of 27 scanned handwriting samples from each of the 90 participants for a total of 2430 handwritten image samples. The model generated from EfficientNet B0 SGD optimization using learning rate of 0.01 at 28th epochs is very good model obtained. Moreover, the model was evaluated for accuracy, precision, recall, and F1−score of 92%. Next, the model is integrated into cloud computing. Furthermore, implementing the best model, android application is developed called Rayuan (Rate Your Handwriting).