<![CDATA[Despite the continuous advancement of digital technology, handwriting still plays an important role, especially in the field of education as a means of evaluating students’ writing skills. However, manual handwriting assessment tends to be subjective and inconsistent, particularly in the aspect of slant, which can reflect the clarity, legibility, and personality of the writer. Therefore, an automated method capable of accurately and objectively detecting handwriting slant is required. This study aims to develop an automated system based on a Convolutional Neural Network (CNN) using the YOLOv5 framework to detect the handwriting slant of university students. The dataset consists of 680 handwriting images annotated into three categories: upright, left-slanted, and right-slanted. The training process was conducted through four main experiments with variations in parameters such as batch size, epoch, and image size. The best model configuration was achieved with a batch size of 16, 150 epochs, and an image size of 640, resulting in an mAP@0.5 score of 0.894 and an F1-score of 0.84 on the training data. Evaluation on the training data showed that the model successfully classified left-slanted handwriting with 97% accuracy, right-slanted with 95%, and upright with 84%. On the test data, the model also demonstrated good performance with an average mAP@0.5 of 0.59, recall of 0.835, and classification accuracies of 100% for left-slanted, 93% for right-slanted, and 57% for upright handwriting. This study demonstrates that the CNN approach using YOLOv5 is effective for handwriting slant detection and has great potential for application in other related fields]]>
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