naina

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Read any document. One C++ core, everywhere.

Try it online · Documentation · Architecture · Roadmap · Model weights

Use it in your browser now → No install, no upload, no account. PDFs and images, Latin/CJK and Devanagari.

Publishing status. The web app and docs are live. The Python, Node and Flutter packages are built and tested but not yet on PyPI, npm or pub.dev — the version badges above go green when they are. Publishing needs one git tag for PyPI and npm, and a cargo publish for crates.io. Build from source meanwhile; see Install.

Packages

Package	Registry	What it does	State
`naina`	PyPI	Python, self-contained wheels	built, unpublished
`@jvoltci/naina`	npm	Node, inference off the event loop	built, unpublished
`@jvoltci/naina-wasm`	npm	Browser, 143 KB brotli	built, unpublished
`naina`	pub.dev	Flutter, FFI, Android + iOS	built; Android verified on-device, iOS unproven
`naina`	crates.io	Rust over the C ABI	built, unpublished
—	—	MCP server for LLM tools	works, in-repo

import naina
import numpy as np
from PIL import Image

img = np.asarray(Image.open("invoice.png").convert("RGB"))

# One-liner: image -> markdown
print(naina.read(img))

# Or keep an Engine around
engine = naina.Engine(tier=naina.Tier.SMALL)
page = engine.read(img)
for line in page.lines:
    print(f"{line.confidence:.3f}  {line.text}")

Why

OCR accuracy is a solved commodity. PP-OCRv6's weights are Apache-2.0, so naina runs the same models PaddleOCR runs and gets the same accuracy. Competing there is unwinnable and pointless.

The gap is distribution. Every existing tool is locked into one lane:

Tool	Lane	Cannot do
PaddleOCR	Python, server	No Node/Rust/browser/C ABI. Training framework first, huge surface
RapidOCR	Multi-language, as separate ports	Behaviour drifts between the Python, C++, Java and .NET versions
oar-ocr	Rust only	No Python/Node bindings, no shipped WASM
retto	Rust only, det+rec only	No bindings, no layout
client-ocr	Browser only	No server, no native
ML Kit (Google Lens)	Mobile only, closed weights	Cannot self-host; 5 scripts only
MinerU / marker / docling	Python, GPU-leaning	Licence traps, heavy installs

Nobody ships one engine that runs identically everywhere. This is llama.cpp's playbook applied to OCR: llama.cpp won on portability and zero dependencies, not on inference math.

No OpenCV. No pyclipper. No PaddlePaddle. The convex hull, minimum-area rectangle, polygon offset and contour tracing are ~450 lines of tested C++, because a 300 MB dependency tree would defeat the point of an 11 MB tier.

What you get

Three device tiers. A size axis, not a licence axis — every model naina ships is Apache-2.0 and safe for commercial use.

Tier	det	rec	layout	Total	Target	Charset
`tiny`	1.8 MB	4.5 MB	4.9 MB	≈ 11 MB	Browser, phone, Pi Zero	6,904 (CJK + Latin)
`small`	9.9 MB	21.2 MB	23.5 MB	≈ 55 MB	Laptop, Pi 5, mobile app	18,708 (50 languages)
`medium`	62.0 MB	76.6 MB	130.5 MB	≈ 269 MB	Server, desktop	18,708 (50 languages)

PaddleOCR ships no ONNX build of the small layout models, so naina converts them itself — byte-deterministically, and verified per-column against the Paddle original. Without that, layout would exist only at the 269 MB tier and an 11 MB browser build could not describe document structure. See tools/paddle2onnx_layout.py.

Every binding over one C ABI, so behaviour cannot drift between languages:

Binding	Status	Install
C / C++	✅ works	`naina.h` — the contract every other binding targets
Python	✅ works, unpublished	build from source; `pip install naina` once released
Node / TypeScript	✅ works, unpublished	build from source; needs a local toolchain
Rust	❌ v0.5	—
WASM / browser	❌ v0.4	—

Weights are mirrored, not borrowed. naina fetches from its own release, not from upstream hosting, so an upstream re-tag or deletion cannot break installs. Every file is pinned by sha256, so a corrupted or substituted download fails closed rather than producing silently wrong output. Provenance for each artifact is recorded in NOTICE and as a source_url in the manifest.

Benchmarks

Real measurements, not vendor claims.

End-to-end, tiny tier, Apple M3 Pro — rendered text fixture, 480×140:

Line	Recognised	Recognition conf	Detection score
1	`HELLO WORLD`	0.967	0.899
2	`naina 2026`	1.000	0.925

Reproduce: ctest --preset macos-arm64 -R test_ocr_e2e --output-on-failure

On the accuracy numbers everyone quotes. Vendors self-report 96.33% on OmniDocBench v1.6 while independent evaluation of the same benchmark tops out around 90.1%. naina will publish per-device numbers with the harness in-repo and the command to reproduce them, or publish nothing. A full benchmark matrix lands with v1.0.

Status

v0.2 — text spotting works end to end. The honest state:

Component	Status
C ABI (`naina_read`, page accessors, stage-level access)	✅
Model registry — manifest-driven, sha256-verified, tier fallback	✅
Detection — PP-OCRv6 det, DBNet decode	✅
Recognition — PP-OCRv6 rec, CTC greedy decode	✅
Geometry — convex hull, min-area rect, polygon offset, no OpenCV	✅
Page storage — pointer-stable, markdown + JSON	✅
Python binding	✅
Node binding	✅
ONNX Runtime backend	✅
NCNN backend	⚠️ compiles, but `FindNCNN.cmake` does not locate a brew install
Recognition batching (one strip per call today)	⚠️ correct but unoptimised
Layout analysis → structured markdown	❌ v0.3
WASM + browser app	❌ v0.4
Rust binding	❌ v0.5
Cross-binding parity enforced in CI	❌ v1.0
MCP server (`mcp/`, 2 tools, verified over stdio)	✅

13 C++ tests, 6 Python tests, 6 Node tests. CI builds on Linux (gcc + clang) and macOS arm64.

Not supported, deliberately: handwriting (PP-OCRv6 is weak at it and claiming otherwise would be dishonest), autoregressive VLM parsing, training, and chart/formula semantic extraction.

Install

Not yet on PyPI or npm — see the note at the top. Build from source:

cmake --preset macos-arm64           # or linux-x86_64, linux-arm64, windows-x86_64
cmake --build --preset macos-arm64
ctest --preset macos-arm64

Requires CMake ≥ 3.24, a C++20 compiler, yaml-cpp, libcurl, and ONNX Runtime.

naina ships no image decoder on purpose — it takes raw pixels. Use Pillow, OpenCV, sharp, or anything else that hands you a buffer.

Environment

Variable	Effect
`NAINA_CACHE`	Where weights are cached. Default `~/.cache/naina/models`
`NAINA_OFFLINE=1`	Disable network; use only what is already cached
`NAINA_REGISTRY`	Path to `registry.yaml`. Both bindings set this automatically

MCP server

An agent can read documents through naina directly:

{
  "mcpServers": {
    "naina": { "command": "npx", "args": ["-y", "@jvoltci/naina-mcp"] }
  }
}

Two tools: read_document (markdown) and read_document_detailed (per-line text, confidence, quads). See mcp/README.md.

Reading a page carries no session state, so the server is written stateless — which is what MCP spec revision 2026-07-28 formalised. Note that the current SDK (1.30.0) only negotiates up to 2025-11-25; the newer revision is a dependency bump away, not a rewrite.

Documentation

Architecture — the C ABI, backends, model registry
Roadmap — what ships when
Design spec — why naina is shaped this way
Contributing

The name

naina (नैना) means eyes in Hindi. The library reads.

It began as a face-recognition runtime under the same name. That work is preserved on the face-stack branch, and the engine it produced — C ABI, backend abstraction, manifest-driven model loader — is what made this pivot cheap.

Contributing

PRs welcome. See CONTRIBUTING.md. Open a Discussion for anything beyond a small fix.

License

Apache-2.0. Redistributed model weights are also Apache-2.0 — see NOTICE.

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naina 0.2.0

Read any document. One C++ core, everywhere.

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MCP server

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