baberu-ocr

About

What baberu-ocr is

Baberu OCR English · 日本語 Baberu OCR reads the text inside a manga speech bubble. One 115M model, three languages. On the public Japanese benchmark it beats the specialist it was distilled from (manga-ocr); on Chinese and English it goes toe-to-toe with a general model 8× its size — all measured on independent ground truth. Most manga OCR is Japanese-only and assumes clean horizontal lines. Real manga bubbles are not that. They run vertical, pack in sound effects, switch scripts mid-sentence, and mix full-width and half-width characters. Baberu was trained for exactly that, in Japanese, Chinese, and English at once. At a glance - Three languages, one checkpoint (ja / zh / en). Lowest character-error-rate of every model compared on Japanese; on Chinese and English it matches a model 8× its size on the typical bubble, and is more robust. - 115M parameters, about one-eighth the size of the 0.9B vision-language models it goes toe-to-toe with. - Character-level (14,630-symbol vocab): sound effects, rare kanji, and full/half-width mixing stay one token each, with no subword fragmentation. - Local and small. Apache-2.0, shipped as a 121 MB or 242 MB ONNX build that needs only onnxruntime, numpy, and pillow. How it works DINOv2 looks, a small decoder writes. The decoder is built from scratch: 6 layers, grouped-query attention, SwiGLU, RMSNorm sandwich, 1D RoPE, tied embeddings. Nothing exotic. What mattered was the training order. Freeze the vision encoder, train the decoder, then unfreeze the encoder and keep training. The accuracy jumps in that second stage, and it saturates fast: around 30k steps, roughly a tenth of an epoch. Character-level output fits the task — the target is a sequence of characters — and avoids the uneven, ballooning tokenizations that subword vocabularies produce on sound effects and mixed scripts (manga-ocr is character-level for the same reason). Benchmarks Teacher labels would grade their own homework, so every number here is measured against independent ground truth, on images the model never trained on. Japanese uses the public human-labeled Manga109-v2026. For Chinese and English we built a fresh held-out set from galleries published after

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

How language models work

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys baberu-ocr for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (baberu-ocr below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/chat/completions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"baberu-ocr","messages":[{"role":"user","content":"Hello"}]}'

Details

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Licence

Open weights

Sources

Weights & code

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