Abstract
A Python-based media workflow that treats manga and comic pages as visual documents, running OCR text extraction, translation, and image-processing stages in sequence to produce translated manga output. The core engineering problem is maintaining the spatial association between extracted text and its originating page regions across every pipeline stage.
1. What This Is
An open experimentation project from the 2026 project set. Manga pages are treated as visual documents rather than plain text files, so the pipeline must handle layout, speech-bubble geometry, and mixed-language text. The workflow chains OCR, translation, and image-manipulation steps so each stage can be iterated independently without reworking the others.
No specific OCR engine, translator, or inpainting model is committed to yet; the project is a scaffold for combining text AI with visual processing on comic media.
2. How It Works
The pipeline is a five-stage sequential flow. Each stage receives the output of the previous one and adds or transforms data before passing it along.
| # | Stage | Input | Tool | Output |
|---|---|---|---|---|
| 01 | Load page | Source manga / comic page image | Python loader | Loaded image in memory |
| 02 | Prepare regions | Loaded image | Image processing | OCR-ready page regions |
| 03 | Extract text | OCR-ready regions | OCR engine | Source text + region metadata |
| 04 | Translate | Source text | Translation stage | Target-language text |
| 05 | Compose output | Translated text + page image | Image processing | Translated manga page |
3. Implementation Notes
3.1 Region-text association
The hardest systems problem in the pipeline is preserving the link between a text block and its bounding region across every stage. Each extracted string must carry a page index, bounding box, and bubble identifier through translation and into the compositing step. If that association breaks at any point, the translated text lands in the wrong speech bubble and the output is unusable.
3.2 Stage independence
The project is structured as a media workflow so OCR, translation, and image-output stages are decoupled. Swapping the OCR engine or the translator should not require touching the region-preparation or compositing code. In practice this means each stage exposes a small, well-defined interface (image in, annotated regions out; text in, translated text out).
4. Constraints
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No committed model choices
OCR engine, translator, and inpainting or rendering approach are all unspecified. The pipeline is a scaffold, not a working system, and no end-to-end output has been produced.
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Region-association fragility
Keeping text-to-region mapping intact across multiple processing passes is the most failure-prone part. A single misaligned bounding box cascades into wrong placement in the final image.
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No public repository
The project exists only as portfolio documentation. No source code, tests, or reproducible artifacts are available for external review.
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Experimental scope
Marked OPEN in the 2026 project set. No benchmark results, accuracy metrics, or production output are documented.
5. Next
- a. Select and benchmark an OCR engine suited to manga text (mixed Japanese/English, varied fonts) and lock the extraction stage.
- b. Define a region-metadata schema (bounding box, page index, bubble ID) that survives all downstream stages without loss.
- c. Prototype the compositing step: remove original text, render translated text into the same region, and verify visual fidelity on a sample page.
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