Читать книгу Machine Vision Inspection Systems, Machine Learning-Based Approaches - Группа авторов - Страница 34

One of the main goals in this research is evaluating the performance of one-shot learning for Sinhala language. Using deep learning approaches is not an option for Sinhala character recognition due to a lack of datasets. Sinhala language has 60 characters, making it a complex alphabet. For each character in Sinhala alphabet, we have added 20 new images to Omniglot dataset. First, we have classified Sinhala characters with a model which was not trained with Sinhala characters and was able to achieve 49% accuracy. After training the model with 5% of the Sinhala dataset, the accuracy is improved to 56%. Considering the languages used in the experiment, Sinhala language has the largest alphabet. Compared to some other languages with a smaller number of characters, the model has given a better accuracy for Sinhala. This could be due to significant visual structural differences between characters.