基于深度CNN的漫画书语音气球检测与分割
Deep CNN-based Speech Balloon Detection and Segmentation for Comic Books
摘要：
我们开发了一种自动检测和分割漫画书中的讲话气球，包括他们的载体和尾巴的方法。我们的方法是基于一个深度卷积神经网络，该网络是在图形叙事语料库的注释页面上训练的。更准确地说，我们使用的是受U-Net架构启发的全卷积网络方法，结合基于VGG-16的编码器。经过训练的模型提供了最先进的性能，F1得分超过0.94。定性结果表明，摆动的尾巴，弯曲的角落，甚至虚幻的轮廓不会构成一个大问题。此外，该模型还学会了区分讲话气球和字幕。我们比较我们的模型，早期的结果，并讨论了一些可能的应用。
英文摘要：
We develop a method for the automated detection and segmentation of speech balloons in comic books, including their carrier and tails. Our method is based on a deep convolutional neural network that was trained on annotated pages of the Graphic Narrative Corpus. More precisely, we are using a fully convolutional network approach inspired by the U-Net architecture, combined with a VGG-16 based encoder. The trained model delivers state-of-the-art performance with an F1-score of over 0.94. Qualitative results suggest that wiggly tails, curved corners, and even illusory contours do not pose a major problem. Furthermore, the model has learned to distinguish speech balloons from captions. We compare our model to earlier results and discuss some possible applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
