Automatic Question Generation From Handwritten Lecture Notes Using Trocr Text Recognition and T5 Language Processing

This research involves the creation and evaluation of a system that allows for text extraction and automatic question generation (AQG) using a T5 and TrOCR pipeline. With the use of a Raspberry Pi 5, web camera, and a touchscreen display, factoid- type questions are created from image captures of single-column handwritten notes that only contain textual information. The T5 large language model (LLM) used was finetuned using the Stanford Question Answering Dataset (SQuAD) for facilitating question generation. The system had a word error rate (WER) of 0.40, a ROUGE-1 score of 0.358, and a question validity rate of 68%. This research helps to promote the ease of creation of learning materials in learner education.