An AI manga translator is a chain of separate jobs, not one model that reads a page and returns English. It has to find the text, recognize the Japanese, put the text in reading order, translate it with page context, erase the original lettering without damaging the art, and letter the new text back into place. “RTL” in this context means right-to-left reading order, and “edge” means running the pipeline on local or constrained hardware instead of a large server. An early mistake carries forward into every later step, so the joints between stages matter as much as any single model. The published work describes detection, ordering and translation context in the most detail. Cleaning and lettering are covered far less.
The pipeline in one table
| Stage | Input | Output | What goes wrong downstream if it fails |
|---|---|---|---|
| 1. Text detection | Full page image | Bounding boxes around bubbles and captions | Missed text stays in the art; merged boxes combine two speakers into one line |
| 2. OCR | Cropped text regions | Japanese character strings | Every later stage works from a wrong sentence |
| 3. Reading order | Panels and text boxes | Dialogue sequence | Lines reach the translator out of story order |
| 4. Translation | Ordered text, plus image and story context | Target-language lines | Speaker, pronoun and omitted-subject meaning gets guessed |
| 5. Text removal | Text masks on the page | Art without source lettering | Smudged line art or broken screentones remain visible |
| 6. Lettering | Cleaned page and translated lines | Finished page | Overflowing or undersized type, or text crossing the art |
| 7. Human review | Finished draft | Final text | Literal phrasing that reads wrongly in the target language |
Stage one: find the text and read it in order
Detect regions before recognizing anything
Recognition works on crops, so something must first say where text is. The COLING 2025 study, “Context-Informed Machine Translation of Manga using Multimodal Large Language Models”, uses text-region detection and clustering to group letter pixels into utterances and produce bounding boxes, then applies Manga OCR to those boxes. Detection and recognition are therefore two components that fail independently. A box can be placed correctly while the OCR misreads it, and good OCR cannot rescue a box that cuts a line in half.
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Vertical and stylized Japanese needs careful crops
The public manga-translation-pipeline repository describes its Japanese OCR stage as running on bounding-box crops, the approach it uses for vertical and stylized text. That is the project’s own design description, not a measured accuracy result. Much of the avoidable error enters at the crop. A crop that is too tight clips the first or last character of a vertical column. A crop that is too loose pulls in a neighboring bubble or a sound effect. Check crop boundaries before you change the OCR model.
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Reading order is a separate stage
Manga is read right to left and, within a panel, generally top to bottom. The repository describes its ordering stage in those terms. The 2025 COLING study handled layout differently. It used Magi for panel detection and order estimation, but reported that Magi was not well suited to Japanese text detection in their use, so they adopted only some of its functions. The lesson is that panel layout and text detection are different jobs, and a tool that handles one well may handle the other poorly.
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A workable version of the ordering logic, written as a sequence of checks rather than a specific library call:
- Detect the panel rectangles on the page.
- Sort panels right to left, then top to bottom.
- Assign each text box to the panel that contains its center point.
- Sort the text boxes inside each panel right to left, then top to bottom.
- Overlay the resulting numbers on the page and check them by eye before translation starts.
Splash pages, panels whose bubbles cross a gutter, and staggered bubbles break this simple sort, which is why the visual check in the last step is not optional.
Translation needs the page, not just the bubble
Translating each bubble in isolation discards information the artist placed on the page: who is speaking, what a pronoun refers to, and what the previous panel established. The 2021 AAAI paper, “Towards Fully Automated Manga Translation” by Hinami, Ishiwatari, Yasuda and Matsui, identifies context-aware, multimodal translation as a core challenge. It describes using information from the manga image and from other speech bubbles, including speaker gender, to resolve lines whose meaning depends on who is talking. The paper also proposes automatic corpus construction and a translation benchmark.
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A running story summary as translation memory
The COLING 2025 study supplies a running story summary as translation context alongside the page content. That gives the model a chapter-level memory a single page cannot provide. The paper presents this as a method. It does not show that a multimodal model resolves ambiguity reliably in every case. Test any such setup on passages where a character’s gender, relationship or name changes across chapters, because those are the places where a summary is most likely to mislead.
Removing text without wrecking the art
Cleaning means erasing the source lettering so the translation can take its place. The hard case is text printed over texture, drawing or screentone, where a simple fill leaves a visible patch. The 2025 COLING study describes inpainting as one way to replace text regions in that situation, and points to a manga-specific method as another route. Its authors state that they did not carry out cleaning or lettering in their own study.
Why general inpainting struggles on manga
The 2021 AAAI paper names EdgeConnect as the inpainting model used to replace text regions. It notes that the edge-first approach is useful for repairing drawing defects. The indexed abstract of “Seamless manga inpainting with semantics awareness” (ACM Transactions on Graphics, 2021) explains why manga does not fit assumptions that work on photographs. Abstract imagery, structural lines and screentones make the scene hard to interpret semantically and make plausible content hard to synthesize. Treat generic photo inpainting as a baseline to verify on your own pages, not as a solved step.
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Screentones break under ordinary resizing
Screentones are repeating dot and line patterns, not photographic noise, which makes them the part of a page most easily damaged by a pipeline that rescales images without accounting for them. The Monash record for Xie et al., “Screentone-Preserved Manga Retargeting” (Computer Graphics Forum), states that screentones degrade when images are resized. Preserving them is hard because their patterns are translation-invariant and do not behave well under ordinary interpolation. A pipeline that upscales a page for OCR and later downscales it for output can alter tone patterns, so check the final size, not just the working size.
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- View the cleaned page at 100 percent and at the final output size.
- Inspect mask edges where text meets line art. A mask that is too wide erases drawing; one that is too narrow leaves source letters visible.
- Compare screentone regions before and after any resize, not only the text areas.
- Look hardest at text placed over gradients, action lines and dense hatching, where a bad fill is easiest to see.
Lettering: fitting the translation back into the page
After cleaning, the translated line must sit inside its bubble or caption box at a size a reader can use. The 2025 COLING study states the objective as maximizing font size while keeping text within the designated region. It also says that, to the authors’ knowledge, no prior work had proposed automatic lettering. The public repository offers dynamic font fitting as one implementation of this objective. That is the project’s own description, and results will depend on your fonts and bubble shapes.
Fitting is a search: try a font size, wrap the text, measure whether the lines fit the box, and step down until they do. Set a minimum size so the search never chooses type too small to read. When even the minimum overflows, flag the bubble for a person instead of shrinking the text further.
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Where people still do the work
Translation quality is the stage where automation is least settled. In a 2023 feature from the Government of Japan, “The AI-Powered Manga Translation Service Sharing Beloved Titles with the World”, Shonosuke Ishiwatari, identified as co-founder and CEO of Mantra, said: “polishing up a translation to convey the nuances of a manga in a natural and enjoyable way is a highly creative process that still requires human skill.” The same feature describes cloud-based collaboration in Mantra Engine.
The feature also reports company figures from that year. Mantra said more than 10 companies had adopted the service, and that it processed 40,000 to 50,000 pages per month, roughly 250 titles. These are company-reported numbers from 2023. They were not audited, and they describe a past throughput rather than a current rate.
Edge and deployment: what is established
The only deployment detail in the public implementation is its setup. The repository describes a resource-constrained Google Colab free-tier environment with a T4 GPU. That describes one project’s environment. It does not show that a T4 is the best choice, what it costs today, or how the same pipeline would perform on a laptop or an edge device.
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For a local or edge deployment, the questions that matter are ones you measure yourself on your own pages:
- Peak GPU or accelerator memory for each stage run on its own: detection, OCR and inpainting.
- Time per page for each stage, so you can see whether OCR or inpainting dominates.
- Whether the models fit within the target device’s memory at the image resolution you use.
- How much human correction each page needs, because correction time is a cost on every page the pipeline gets wrong.
Local execution keeps pages on your own hardware. Cloud execution makes shared review simpler, which is the model the 2023 Mantra description puts forward. Choose based on who needs to review the output and where the pages may be stored.
Quick Recap
What the published evidence does not establish
- No independent measurement of OCR accuracy, inpainting quality or screentone damage on a shared page set. The sources describe methods and design choices, not controlled results.
- No controlled comparison of OCR engines, inpainting models or accelerators. A fair comparison needs a common test set, and none of the sources provides one.
- The repository’s examples are illustrative and were not run as controlled tests.
- The 2021 AAAI system and the 2025 COLING method are different systems, and neither is a current product. Model versions change, so read the stage descriptions as architecture, not as a specification of a tool you can install today.
- The repository was accessed on 7 October 2026. Its code, setup notes and any hosted service may have changed since.
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