<![CDATA[Manga has complex visual characteristics, such as variations in speech bubble shapes, diverse text orientations, and dense background illustrations, which complicate the automatic text area detection process. Detection errors in the form of false positives and false negatives can cause text areas to be localized inaccurately and affect the processing at subsequent stages. This study compares the performance of YOLOv8m and YOLOv11m in detecting text areas in Japanese manga using the Intersection over Union (IoU) metric. The dataset consists of 551 manga images annotated into three classes, namely clean_text, messy_text, and text_bubble. Both models were trained under the same parameter configuration for 60 epochs to ensure an objective comparison. The evaluation was performed on 50 test images covering 564 text objects. The test results show that YOLOv11m obtained an average IoU of 0.7598, which is higher than YOLOv8m (0.7196). In addition, YOLOv11m exhibited a faster inference time of 1158.72ms compared with 1276.23ms for YOLOv8m. Based on these results, YOLOv11m demonstrated superior performance over YOLOv8m in terms of both localization accuracy and computational efficiency for the Japanese manga text area detection task.]]>
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