ocracy

One façade over many OCR engines — plus a ledger to help you choose between them.

OCR ("read the text in this image") is solved a dozen ways: local engines (Tesseract, EasyOCR, PaddleOCR), cloud APIs (Google Vision, AWS Textract, Azure), and VLM readers — each with its own install, API, pricing, and language coverage. ocracy gives you a uniform call, a browsable catalog of every option, and the tools to wrap any of them.

import ocracy

text = ocracy.read_text("scan.png")  # just the text, default backend
result = ocracy.ocr("scan.png")  # full result: text + word boxes + confidence
print(result)  # -> the recognized text
for word in result.words:
    print(word.text, word.bbox.as_tuple, word.confidence)

Install

import ocracy is dependency-free. Install only the backends you use, via extras:

pip install "ocracy[tesseract]"     # local, free (also needs the system `tesseract` binary)
pip install "ocracy[easyocr]"       # local, pip-only
pip install "ocracy[google-vision]" # cloud API
pip install "ocracy[table]"         # pandas, for catalog.to_dataframe()

Backends that ship today

The Blocks column is the one people get caught by: each backend reports exactly one granularity, so result.words is empty on a line-level backend and result.lines is empty on a word-level one — an empty list, not an error. See Granularity just below.

…plus 49 more engines/services catalogued in the ledger that you can turn into a working façade with one command (see Add a backend below).

Granularity: which backends fill .words, which fill .lines

Every backend emits exactly one granularity level. Nothing promotes words into lines or splits lines into words, and result.words / result.lines are just filters over the one flat blocks list — so asking for the level a backend does not produce returns [], silently:

r = ocracy.ocr("scan.png", backend="rapidocr")
r.lines  # populated
r.words  # [] — rapidocr reports lines, and nothing raises

r = ocracy.ocr("scan.png", backend="tesseract")
r.words  # populated
r.lines  # [] — tesseract reports words

• word-level (.words populated, .lines empty): tesseract, google-vision, aws-textract, azure-document-intelligence, ocr-space.
• line-level (.lines populated, .words empty): easyocr, rapidocr, paddleocr, ocrmac.
• text only (blocks == [], so both are empty and mean_confidence is None): claude-vision, gpt-4o-vision, mathpix, mistral-ocr, pix2tex-latex-ocr, trocr-handwritten. Use result.text / result.markdown.

Iterating a result (for block in result:) yields whatever units that engine gave you, at its own level — it is not "lines by default".

The ledger's bounding_boxes flag describes the engine, not ocracy's adapter. claude-vision, mathpix and mistral-ocr are all bounding_boxes: true (their APIs can return geometry) while ocracy's façades for them return text only. Treat ocracy.find(bounding_boxes=True) as "engines worth wrapping for geometry", and the list above as what you get today. If your code needs a level, assert it:

result = ocracy.ocr(img, backend=chosen)
if not result.words:
    raise ValueError(f"{chosen} does not report word-level boxes")

Getting a backend running

Some backends need more than pip install (Tesseract's system binary, Paddle's framework, GPU wheels, first-run model weights, or an API key). ocracy turns that into structured, OS-aware guidance — handy for humans and AI agents alike:

ocracy.doctor()  # what's usable now vs what each missing one needs
ocracy.check("paddleocr")  # -> True/False (usable right now?)
print(
    ocracy.requirements("paddleocr").instructions()
)  # exact plan: pip + system deps + GPU + weights
ocracy.install(
    "rapidocr", yes=True
)  # run the pip install + verify (yes=False = dry run)

ocracy doctor · ocracy requirements paddleocr --gpu · ocracy install rapidocr --yes

requirements() even suggests a lighter alternative when one exists (e.g. PaddleOCR → RapidOCR). The ocracy-install-backend skill drives all of this.

Three tiers of access

From simplest to most powerful — reach for the next tier only when you need it:

ocracy.ocr(img)  # 1. facade, default (installed) backend
ocracy.ocr(img, backend="easyocr", languages=["en", "fr"])
ocracy.services.tesseract.read(img, psm=6)  # 2. pick a backend explicitly
ocracy.services.tesseract.adapter  # 3. the raw engine adapter, full control

Every backend returns the same OcrResult, so your code doesn't change when you switch engines: result.text, result.words / result.lines, each block's .bbox and .confidence (normalized to 0..1), result.mean_confidence, result.markdown (when a backend produces it), and result.raw (the untouched engine output).

The shape is uniform; the granularity is not. Each engine fills either .words or .lines (or neither), and the other is an empty list rather than an error — see Granularity before writing code that depends on one.

Command line

Install the CLI extra (pip install "ocracy[cli]") for an ocracy command:

ocracy read scan.png                          # print recognized text
ocracy read scan.png --backend easyocr --languages en,fr
ocracy read scan.png --output json            # text + blocks (boxes, confidence)
ocracy backends                               # backends you can run now
ocracy backends --capability math
ocracy status                                 # readiness table: all / implemented / set-up / tested
ocracy status --level set_up --names          # one level + the "Name (website)" lists
ocracy status --run-tests                     # also OCR-test set-up backends (real calls for remotes)
ocracy find --local --free --handwriting      # browse/filter the ledger
ocracy info google-vision                     # a backend's full record
ocracy scaffold surya                          # start a new façade from the ledger
ocracy validate tesseract                      # smoke-test a backend

Everything is also reachable as python -m ocracy <command>. cw builds the parser from the function signatures in ocracy/tools.py; argcomplete gives tab completion when activated.

The ledger — choose a backend with eyes open

ocracy ships a curated, data-driven catalog of every engine we researched — not only the ones with a working façade — so you can compare on the axes that matter (local vs remote, price, accuracy, languages, handwriting, math, tables, privacy). The data lives in ocracy/data/backends.json, separate from code.

ocracy.catalog  # <Catalog … backends … implemented …>
ocracy.catalog["google-vision"]  # one backend's full record
ocracy.find(is_local=True, open_source=True)  # filter the ledger
ocracy.find(handwriting="yes", is_remote=True)
ocracy.catalog.can("math")  # engines that read formulas
ocracy.catalog.supports_language("Arabic")
ocracy.find(implemented=True)  # only what ocracy can run today
ocracy.catalog.to_dataframe()  # browse as a pandas table
ocracy.catalog.compare(["tesseract", "google-vision", "mathpix"])

implemented is computed live from the code, so the ledger never lies about what actually runs. See ocracy/data/SCHEMA.md for every field. The full cited research report behind the ledger is in misc/docs/ocr_landscape_research.md.

Getting API keys (remote backends)

Remote backends need a credential in an environment variable. ocracy tells you exactly which one — with a link to get it — the moment it's missing:

>>> ocracy.ocr("receipt.png", backend="ocr-space")
MissingCredentialError: No credential found for ocr-space (set one of: OCR_SPACE_API_KEY).
How to get a credential for ocr-space: Register a free API key by email; free tier
allows 25,000 requests/month. Get a key: https://ocr.space/ocrapi/freekey

The same guidance (and links for AWS, Azure, Mistral, OpenAI, Anthropic, Gemini) lives in ocracy.credentials.CREDENTIAL_GUIDANCE, and powers the dynamic errors. Tip: drop the variables in a .env file — ocracy soft-loads it via python-dotenv if that package is installed.

Add a backend (wrap any engine in minutes)

The catalog is large; ocracy ships façades for a curated subset and gives you the machinery to add any other one. Scaffold a backend straight from its ledger entry, fill in the adapter, and validate it:

from ocracy.make_backend import scaffold_backend, validate_adapter

scaffold_backend(
    "mathpix"
)  # creates ocracy/backends/mathpix/ prefilled from the ledger
# ... implement adapter.py's _read (call the engine, return an OcrResult) ...
validate_adapter("mathpix")  # smoke-test it end to end

The full, step-by-step process — including the adapter contract, input/output normalization helpers, credential handling, and packaging — is captured as an agent skill at ocracy/data/skills/ocracy-add-backend/SKILL.md. The ocracy/backends/tesseract/ package is a complete worked example.

Agent skills (Claude Code & other AI agents)

ocracy ships four Anthropic-style skills (under ocracy/data/skills/, installed with the package) so AI coding agents can both use and extend it:

In a repo using Claude Code they're discoverable via the .claude/skills/ symlink bridge; they also travel with pip install ocracy under ocracy/data/skills/.

How it works

• ocracy/base.py — the normalized types (OcrResult, TextBlock, BBox).
• ocracy/catalog.py — the ledger reader/filter (ocracy/data/backends.json).
• ocracy/registry.py — lazy backend discovery + default selection.
• ocracy/services.py — the three-tier service layer.
• ocracy/make_backend.py — the façade-building toolkit (BaseOcrAdapter, make_block, scaffold_backend, validate_adapter).
• ocracy/backends/<id>/ — one subpackage per engine (config.py + adapter.py).
• ocracy/credentials.py — credential resolution for remote backends.
• ocracy/tools.py + ocracy/__main__.py — the ocracy CLI (cw).

The architecture mirrors the sibling façade packages denote (audio→symbol) and aix (LLM providers).