AI in Manga Localization: Segmentation, OCR, and Redrawing

This article provides a quick overview of how artificial intelligence is transforming the manga and comic localization industry. Specifically, it explores the core technologies behind automated workflows: speech bubble segmentation for isolating dialogue areas, vertical optical character recognition (OCR) for extracting complex scripts, and generative AI for background redrawing to repair artwork once the original text is removed.

Speech Bubble Segmentation

The first step in automated comic localization is identifying where the text is located. AI tools use computer vision—specifically instance and semantic segmentation models like Mask R-CNN and U-Net—to scan a comic page and isolate speech bubbles from the surrounding artwork.

Unlike standard document scanners, these models are trained on thousands of manga and comic pages to recognize the unique, irregular shapes of dialogue balloons, thought clouds, and narration boxes. The AI creates a precise pixel-level mask of the interior of each bubble. Furthermore, advanced segmentation models can differentiate between text trapped inside a bubble and floating text (such as sound effects or "onomatopoeia") drawn directly over the background art. This classification dictates how the software handles the subsequent text removal and redrawing phases.

Vertical Optical Character Recognition (OCR)

Once the text areas are isolated, the localization tool must read and extract the original dialogue. Standard OCR engines fail when applied to Japanese manga or Chinese manhua because they are programmed to read horizontal text from left to right.

Localization tools solve this by utilizing specialized deep learning OCR models trained for vertical typography. These engines combine Convolutional Neural Networks (CNNs) to identify intricate characters—such as dense Kanji, Hiragana, and Katakana—with Recurrent Neural Networks (RNNs) or Vision Transformers (ViTs) to understand the sequence of the text. The AI reads the columns from top to bottom and processes the lines from right to left. Additionally, these models are trained on stylized, hand-drawn comic fonts, allowing them to accurately digitize text even when it is bolded, warped, or italicized for dramatic effect.

Background Redrawing (Inpainting)

Removing the original text leaves a blank void. If the text was inside a standard white speech bubble, the tool simply fills the area with white pixels. However, when translating sound effects or floating text that sits directly on top of complex artwork, removing the text destroys part of the illustration.

Historically, human editors had to manually redraw these missing lines and textures. Today, AI localization tools automate this through a process called image inpainting, powered by Generative Adversarial Networks (GANs) or Diffusion models.

When the segmentation model masks out the original floating text, the inpainting AI analyzes the pixels immediately surrounding the blank space. It looks at the artist's line weights, screentone patterns, cross-hatching, and color gradients. The AI then generates new artwork to seamlessly bridge the gap, reconstructing the missing background as if the original text was never there. Once the background is restored, the translated text can be typeset cleanly over the newly repaired artwork.