eka-pii-redaction 0.1.0

Detect and redact text + visual PII in document images (LayoutLMv3 + YOLO).

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title: Eka PII Redactor emoji: 🛡️ colorFrom: blue colorTo: purple sdk: docker app_port: 7860 pinned: false

Eka-PII-redaction

Detect and redact PII in document images — both text PII (names, addresses, IDs, dates, phone/email, …) and visual entities (signatures, stamps/seals, QR/barcodes, face photos, fingerprints, logos) — behind a single class.

• One install, one Hugging Face repo. The package internally pulls two model weights (a LayoutLMv3 text classifier + a YOLO visual detector) from one HF repo; you only ever reference one repo id.
• Text + visual in one call, or text-only if you don't need the detector.
• CPU or GPU — auto-selects CUDA if available, else CPU.
• Choose what to redact — exclude any categories; everything is on by default.
• Returns a structured entity list (text, bbox, category, …) and/or a redacted image.

Install

pip install eka-pii-redaction          # core library
pip install "eka-pii-redaction[server]"  # + FastAPI service

System dependency: Tesseract OCR (used for the text model).

# Debian/Ubuntu
sudo apt-get install -y tesseract-ocr
# macOS
brew install tesseract

(The provided Docker image installs everything for you — see below.)

Quickstart

from eka_pii_redaction import ImagePIIRedactor

# Loads both models from one HF repo; GPU if available, else CPU.
redactor = ImagePIIRedactor(
    "ekacare/pii-redactors",
    detect_visual=True,          # set False to skip YOLO download + visual detection
    # device="cpu",              # force CPU (default: auto)
    # exclude_entities=["logo", "brandname"],  # never redact these
)

# 1) Get the structured entity list
for e in redactor.detect("page.jpg"):
    print(e.kind, e.category, e.bbox, e.text, e.score)

# 2) Get a redacted image (PIL.Image)
redactor.redact("page.jpg").save("redacted.png")
redactor.redact("page.jpg", mode="blur").save("blurred.png")  # solid|blur|pixelate

detect() / redact() accept a file path, raw bytes, or a PIL.Image.

Text-only PII (plain strings)

from eka_pii_redaction import TextPIIRedactor

r = TextPIIRedactor("ekacare/pii-redactors")     # GPU if available, else CPU

# 1) Character-span entities
for s in r.detect("John Doe, DOB 1990-01-01, [e-mail]"):
    print(s.category, s.l1, s.start, s.end, s.text, s.score)

# 2) Redacted string (mask supports {category} / {l1} placeholders)
r.redact("Call John at [e-mail]", mask="[REDACTED]")    # -> "Call [REDACTED] at [REDACTED]"
r.redact("Call John at [e-mail]", mask="[{category}]")  # -> "Call [primary_subject_name] at [email]"

TextPIIRedactor runs a multilingual MiniLM token classifier on raw text — no OCR, no image — and returns TextPIISpan(category, start, end, l1, text, score) with character offsets.

API

ImagePIIRedactor(hf_repo, *, detect_visual=True, device=None, exclude_entities=None, visual_score_threshold=0.25, ocr_lang=None, cache_dir=None)

arg	meaning
hf_repo	HF repo id or a local dir with text_model/ + visual_model/best.pt.
detect_visual	If False, YOLO weights are not downloaded or loaded — text PII only.
device	"cuda" / "cpu". None → auto (CUDA if available).
exclude_entities	Categories to never detect/redact. Default: none excluded (all on).
visual_score_threshold	YOLO confidence cutoff.
ocr_lang	Tesseract language/script, e.g. "eng", "eng+Devanagari".

detect(image, *, exclude_entities=None, ocr_lang=None) -> list[PIIEntity]

Each PIIEntity has:

field	description
category	fine category, e.g. primary_subject_name, signature
kind	"text" or "visual"
bbox	[x0, y0, x1, y1] in original-image pixels
l1	coarse group: person/location/contact/uid/… or biometric_visual
text	OCR text (text entities) or None (visual)
score	confidence in [0,1]

redact(image, *, mode="solid", color=(0,0,0), exclude_entities=None, ocr_lang=None, pad=2) -> PIL.Image

Returns a redacted copy. mode ∈ solid | blur | pixelate.

ImagePIIRedactor.list_entities() -> {"text": [...], "visual": [...]}

All selectable categories.

Categories

• Text (47): person (name, age, gender, occupation, …), location (address, city, state, postcode, …), date_time, contact (phone, email, web_url, fax), uid (aadhaar, pan, passport, mrn/uhid, abha, insurance policy, bank/iban/upi, …), device_net, credential, and brandname.
• Visual (6): signature, seal_stamp, qr_barcode, face_photo, fingerprint_thumb_impression, logo.

ImagePIIRedactor.list_entities() returns the exact set.

Run as a container

The image bundles torch+CUDA, Tesseract, the FastAPI service, and the React demo UI (web/, built at image-build time and served by the same process at /). Same image runs on GPU or CPU. This is also what runs on the ekacare/pii-redactor-demo HF Space.

docker build -t eka-pii-redaction .

# GPU
docker run --gpus all -p 7860:7860 \
  -e EKA_PII_HF_REPO=ekacare/pii-redactors \
  -v $HOME/.cache/huggingface:/root/.cache/huggingface \
  eka-pii-redaction

# CPU
docker run -e EKA_PII_DEVICE=cpu -p 7860:7860 eka-pii-redaction

Open http://localhost:7860 for the UI. API endpoints: GET /health, GET /entities, GET /entities-text, POST /detect / POST /redact (multipart file, optional exclude query param, mode/color form fields for /redact), and POST /detect-text / POST /redact-text (JSON {"text": ..., "exclude": [...]}). Env: EKA_PII_HF_REPO, EKA_PII_DETECT_VISUAL, EKA_PII_DEVICE, EKA_PII_EXCLUDE.

curl -F file=@page.jpg http://localhost:7860/detect
curl -F file=@page.jpg -F mode=blur http://localhost:7860/redact -o redacted.png

Structure (by modality)

The library is organized by modality, so additional models slot in cleanly:

eka_pii_redaction/
  taxonomy.py, entities.py          # shared
  image/                            # IMAGE modality (implemented)
    redactor.py  -> ImagePIIRedactor
    layoutlmv3.py -> text-PII-in-image detector
    yolo11m.py    -> visual-entity detector
  text/                             # TEXT modality (implemented)
    redactor.py  -> TextPIIRedactor  (PII inside plain-text strings, no image)
    minilm.py    -> MiniLM token classifier (char-span detector)

The single model repo mirrors this:

<hf_repo>/
  image/ layoutlmv3/   yolo/best.pt
  text/  minilm/                    # multilingual MiniLM text-PII model

Both modalities share the category taxonomy (eka_pii_redaction.taxonomy); the text model also detects mac_address (device_net).

Deploying the demo to HF Spaces

The Space (ekacare/pii-redactor-demo) runs this same repo's Docker image — see the "Run as a container" section above. To (re)deploy:

1. Create the Space once, as private (matches the model's current visibility — flip both to public together later):

   huggingface-cli repo create pii-redactor-demo --organization ekacare \
     --type space --space_sdk docker --private
   

   (or via the HF UI: New Space → owner ekacare → SDK Docker → Private.)
2. Add the model's read token as a Space secret: Space → Settings → Repository secrets → add HF_TOKEN. The server already reads it via huggingface_hub's standard auth — no code change needed.
3. Push this repo to the Space's git remote:

   git remote add space https://huggingface.co/spaces/ekacare/pii-redactor-demo
   git push space add-streamlit-tester:main   # or whichever branch is ready
   

4. Watch the build under the Space's "Logs" tab. The base image (pytorch/pytorch:...-cuda12.1-cudnn9-runtime) is large, so the first build can take a while; subsequent pushes reuse Docker layer caching.
5. Once it shows Running, open the Space URL and click through both tabs.

Publishing the model weights

The trained checkpoints are assembled into the single HF repo with:

python scripts/build_hf_repo.py \
  --layoutlmv3   .../checkpoints/base_v3_combined_4ep/final \
  --yolo-weights .../checkpoints/visual/visual_yolo11m/weights/best.pt \
  --out /tmp/eka-pii-hf --push --repo-id ekacare/pii-redactors

How it works (image modality)

1. Text-in-image (LayoutLMv3): Tesseract OCR (via the processor) → words + boxes → LayoutLMv3 token classifier → per-word BIO labels → merged spans.
2. Visual (YOLO): detector over the page → boxes + categories.
3. Redact: fill / blur / pixelate every selected entity's box.