Peixoto, Helton MaiaSilva Filho, Darlan de Castro2022-08-232022-08-232022-07-22SILVA FILHO, Darlan de Castro. Reconhecimento de caracteres utilizando redes neurais convolucionais para auxiliar nas Correções do Sistema multiprova. 2022. 62f.Trabalho de Conclusão de Curso (Graduação em Engenharia de Computação) - Departamento de Engenharia de Computação e Automação, Centro de Tecnologia, Universidade Federal do Rio Grande do Norte, Natal, 2022.https://repositorio.ufrn.br/handle/123456789/49203The Multiprova software was widely used during remote teaching at the Federal University of Rio Grande do Norte. With the return of classroom classes, the exam correction process needed to be improved to facilitate the adaptation of the system to presential courses, one of these improvements was the automatic correction of student response cards. Optical character recognition is a recent technique widely used for machines to read text written by humans. This academic work aims to develop a handwritten character recognition process to improve and facilitate exam correction in Multiprova software. For this purpose, convolutional neural networks were used to perform this task. With the UFRN student’s images, different scenarios were analyzed and compared with the increment of network configurations to generate neural networks with the best accuracy rates. As a result, excellent levels of accuracy were obtained, allowing high reliability in the software and more security and ease in exam correction.Reconhecimento ótico de caracteresInteligência artificialAprendizado de máquinaRedes neurais convolucionaisbachelorThesis