marksense 0.2.0

Read marked paper forms (bubble sheets, surveys, checklists, exams) into structured data — locally, no cloud.

marksense

Read marked paper forms — bubble sheets, surveys, checklists, exams — into structured data (JSON/CSV). Runs locally: no cloud, no account, no telemetry.

Point marksense at a scan or phone photo of a filled form plus a template you define once, and it returns every answer with a confidence score:

marksense read examples/samples/quiz_filled_01.png -t examples/templates/quiz.json

{
  "form_type": "quiz",
  "source": "examples/samples/quiz_filled_01.png",
  "answers": {
    "Q1": "A",
    "Q2": "D",
    "Q3": "D"
  }
}

(output truncated — the full result carries all 20 answers plus per-question confidence, multi-mark flags, and per-page alignment confidence)

Why marksense

• Any layout. Layout knowledge lives in a template JSON, not in code — checkboxes, bubbles, grids, multi-page forms, mixed mark types. Adding a form means writing JSON, never code.
• Robust to real-world scans. Every page is aligned onto the blank template (feature matching
  • ECC refinement) before detection, so skewed scans and phone photos read correctly.
• Model-free by default, learned detection when you want it. A pixel-density detector works out of the box with zero downloads; a small ONNX mark-detection model can be plugged in (--model / --download) for harder real-world scans.
• Lean runtime. onnxruntime, OpenCV, NumPy, PyMuPDF. No PyTorch, no GPU needed.

Install

pip install marksense

Quickstart

The repository ships a self-contained synthetic demo (generated by examples/generate_samples.py):

# Read one form -> JSON on stdout
marksense read examples/samples/quiz_filled_01.png -t examples/templates/quiz.json

# A whole stack -> one CSV row per form
marksense batch examples/samples/ -t examples/templates/survey.json -o results.csv

# Check a template you are authoring
marksense template validate examples/templates/quiz.json

Or from Python:

from marksense import read_form

result = read_form("scan.jpg", template="my-form.json")
result.answers                # {"Q1": "3", ...}
result.confidence             # per-question confidence
result.multi_marked           # questions with more than one mark (review these)
result.to_csv()               # question,answer,confidence,flags

Run as a service

pip install "marksense[service]"
marksense serve                       # http://127.0.0.1:8000, bundled demo templates
marksense serve --templates-dir ./my-templates --port 9000

Or with Docker:

docker build -t marksense .
docker run -p 8000:8000 -v ./my-templates:/templates marksense

Endpoints: GET /health, GET /templates, POST /read?form_type=<name> (multipart file). Interactive docs at /docs.

Reading your own forms

1. Get a clean image of the blank form (render the PDF or scan an empty copy).
2. Write a template JSON describing where each option is — see the template authoring guide.
3. marksense template validate my-form.json, then marksense read.

How it works

input (PDF / image)
  └─ render pages ──> align to template ──> detect marks ──> map to answers
                      (ORB → SIFT → ECC)    (ONNX YOLO or     (nearest ROI +
                                             density fallback)  confidence)

The detector only knows two things: what a check looks like and what a circle looks like. All form-specific knowledge — page sizes, question positions, answer values — is declarative template JSON. That separation is what makes the engine general.

v0.1 uses the density detector by default — no downloads, fully offline. Learned-model weights trained on public datasets ship in an upcoming release; pass --download / auto_download=True to fetch them once published (cached under ~/.marksense/models/), or --model path/to/weights.onnx to use your own.

Roadmap

• Self-hosted REST service + Docker image (shipped in v0.2)
• Published accuracy benchmarks
• Clean-provenance model weights (training pipeline and guide: docs/training.md)
• Template authoring helpers (auto-detect form regions)

Development

git clone https://github.com/RoyAbra27/marksense
cd marksense
uv venv && uv pip install -e ".[dev]"
pytest            # full suite runs with no model file and no network
ruff check .

Design docs live in docs/design/; start with 0001-marksense-v1.

License

MIT — see LICENSE.