Hence, we decided to change our current version of the dataset with the new version, and then re-train our improved model architecture on it.
Our model architecture shown in Figure 3.ĭuring the improved model development, there was a new update on the origin dataset. Finally, we successfully developed a model that produces 92% training accuracy and 89% validation accuracy. However, we find the model still has trouble maintaining the validation accuracy, so we use L2 kernel regularizer on each convolution layer, and use additional callbacks to reduce the learning rate when validation accuracy gets plateaued.Īs a result, we have got more stable validation accuracy. We use batch normalization, dropout, and global average pooling to reduce overfitting. Hence, we aim to create an improved model that can reduce the overfitting issue. In our experiment, the accuracy on the training set keeps increasing, while the accuracy on the validation set stays around 80%. To the best of our knowledge, our baseline CNN model tends to overfit.
Of the many we got, we chose the Arabic Character Recognition project as our model reference, it is publicly available as an open-source project on GitHub. In order to develop a good model, we have searched several research papers and open-source projects on Handwritten Character Recognition topics as our references. Our baseline model produces 98% training accuracy and 88% validation accuracy. We build our baseline model based on basic Convolutional Neural Network architecture (see Figure 2.) with an additional 128 fully connected neurons layer. The characters are shown in the picture below. The dataset contains twenty ancient javanese alphabet characters which are, ha, na, ca, ra, ka, da, ta, sa, wa, la, pa, dha, ja, ya, nya, ma, ga, ba, tha, nga. Thanks to Phiard the author of the dataset. In these projects, we build a handwritten character recognition model that recognizes the ancient javanese alphabet ( Aksara Jawa), based on a public dataset available on Kaggle. This project is our final project for Google Bangkit Academy.