Service for Checking Students’ Written Work Using a Neural Network

The article discusses the necessity of automating the process for checking and evaluating students’ written work in educational institutions. It proposes the recognition of handwritten texts through computer vision methods, specifically by considering the application of convolutional neural networks (CNNs). An architecture for a CNN capable of recognizing letters from both the English and Russian alphabets is developed. The network is trained on extended datasets to enhance recognition accuracy and prevent overfitting. The authors have designed a conceptual model for an information system that processes students’ tests via an adaptive website. This includes various user scenarios, context diagrams for uploading test answers, and procedures for text recognition in images. The article also deconstructs the contextual structure of the written work assessment process. Automating the checking of written work promises to expedite and simplify the assessment process for students’ assignments.