# Object Detection for Comics using Manga109 Annotations

arXiv: https://arxiv.org/abs/1803.08670 · HTML: https://mlsanity.com/paper/1803.08670/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2018.

## Built on by (most cited first, 14 of 14)

- [The Manga Whisperer: Automatically Generating Transcriptions for Comics](https://mlsanity.com/paper/2401.10224/) (2024)
- [CoMix: A Comprehensive Benchmark for Multi-Task Comic Understanding](https://mlsanity.com/paper/2407.03550/) (2024)
- [Tails Tell Tales: Chapter-Wide Manga Transcriptions with Character Names](https://mlsanity.com/paper/2408.00298/) (2024)
- [USB: Universal-Scale Object Detection Benchmark](https://mlsanity.com/paper/2103.14027/) (2021)
- [Unsupervised Manga Character Re-identification via Face-body and Spatial-temporal Associated Clustering](https://mlsanity.com/paper/2204.04621/) (2022)
- [One missing piece in Vision and Language: A Survey on Comics Understanding](https://mlsanity.com/paper/2409.09502/) (2024)
- [Comics Datasets Framework: Mix of Comics datasets for detection benchmarking](https://mlsanity.com/paper/2407.03540/) (2024)
- [From Panels to Prose: Generating Literary Narratives from Comics](https://mlsanity.com/paper/2503.23344/) (2025)
- [ComicGAN: Text-to-Comic Generative Adversarial Network](https://mlsanity.com/paper/2109.09120/) (2021)
- [CAST: Character labeling in Animation using Self-supervision by Tracking](https://mlsanity.com/paper/2201.07619/) (2022)
- [Character-Centric Understanding of Animated Movies](https://mlsanity.com/paper/2509.12204/) (2025)
- [Domain-Adaptive Self-Supervised Pre-Training for Face & Body Detection in Drawings](https://mlsanity.com/paper/2211.10641/) (2022)
- [ComicScene154: A Scene Dataset for Comic Analysis](https://mlsanity.com/paper/2508.16190/) (2025)
- [Re:Cognize -- Open-Set Comic Character Re-Identification](https://mlsanity.com/paper/2609.34032/) (2026)

## Abstract (arXiv)

With the growth of digitized comics, image understanding techniques are becoming important. In this paper, we focus on object detection, which is a fundamental task of image understanding. Although convolutional neural networks (CNN)-based methods archived good performance in object detection for naturalistic images, there are two problems in applying these methods to the comic object detection task. First, there is no large-scale annotated comics dataset. The CNN-based methods require large-scale annotations for training. Secondly, the objects in comics are highly overlapped compared to naturalistic images. This overlap causes the assignment problem in the existing CNN-based methods. To solve these problems, we proposed a new annotation dataset and a new CNN model. We annotated an existing image dataset of comics and created the largest annotation dataset, named Manga109-annotations. For the assignment problem, we proposed a new CNN-based detector, SSD300-fork. We compared SSD300-fork with other detection methods using Manga109-annotations and confirmed that our model outperformed them based on the mAP score.