Читать книгу Machine Vision Inspection Systems, Machine Learning-Based Approaches - Группа авторов - Страница 28

This study focuses on character domain. Therefore, we use the Omniglot dataset to train the model to learn a discriminative function and features of the images. Omniglot dataset consists of 1,623 handwritten characters that belong to 50 alphabets [6]. Each character has 20 samples, which is written by 20 individuals through the Amazon Mechanical Turk platform. The dataset is divided into a training set with 30 alphabets and test set with 20 alphabets. For the training sessions, we use data from the training set only and validate using the data in the test set.