Transcrição automatizada de algoritmos manuscritos: reduzindo esforços com inteligência artificial

Technology has transformed the educational context, making life easier for young people, civil servants, and teachers. When it comes to learning, writing enhances information retention and improves memorization. However, in the area of information technology, programming requires codes to be typed and tested in compilers and translators, which can compromise the efficiency of students when transposing handwritten algorithms to the digital environment. In this context, it is worth mentioning activities that require manual writing of codes or drafts, whether due to preference or limitations of computational resources, which are challenging for teachers, who need to correct them manually. These activities require significant cognitive effort or testing of their drafted solutions. Therefore, to solve this problem, we present this study as a computational tool based on Machine Learning aimed at facilitating and automating the process of transcribing handwritten algorithms into executable code. This tool facilitates the correction of activities, reduces the effort of correcting tests by teachers, and promotes the practical exploration of ideas in the development of algorithms.