indic-pii

indic-pii is a lightweight Python library for detecting and redacting Indian-specific personally identifiable information (PII) from free-form text.

It focuses on common identifiers used in India and exposes a small API for:

• Detecting PII spans with confidence scores
• Redacting matched values with default or custom labels
• Optionally using spaCy NER for names, locations, and organisations

Supported PII Types

The library currently detects:

• Aadhaar numbers
• PAN numbers
• UPI IDs
• Indian mobile numbers
• Bank account numbers
• IFSC codes
• Passport numbers
• Voter IDs
• Driving licence numbers
• Email addresses
• Dates of birth

With optional NER support enabled, it can also detect:

• Person names
• Locations
• Organisations

Installation

Install the base package:

pip install indic-pii

Install with optional NER support:

pip install "indic-pii[ner]"

If you want NER detection to work, you will also need a spaCy model, for example:

python -m spacy download en_core_web_sm

Quick Start

from indic_pii import PIIDetector

text = (
    "Rahul's Aadhaar is 3043 3218 1964, PAN is ABCDE1234F, "
    "and phone number is +91 9876543210."
)

detector = PIIDetector(use_ner=False)

matches = detector.detect(text)
for match in matches:
    print(match.pii_type, match.value, match.confidence)

print(detector.redact(text))

You can also use the functional API:

import indic_pii

matches = indic_pii.detect("Send to rahul@upi")
redacted = indic_pii.redact("Send to rahul@upi")

Example Output

[
    PIIMatch(type='AADHAAR', value='3043 3218 1964', span=(18, 32), confidence=1.0),
    PIIMatch(type='PAN', value='ABCDE1234F', span=(41, 51), confidence=0.9),
    PIIMatch(type='PHONE', value='+91 9876543210', span=(72, 86), confidence=0.9),
]

Redacted text:

Rahul's Aadhaar is [AADHAAR_REDACTED], PAN is [PAN_REDACTED], and phone number is [PHONE_REDACTED].

Custom Redaction Labels

from indic_pii import PIIDetector

detector = PIIDetector(use_ner=False)
text = "Email user@example.com or call 9876543210"

redacted = detector.redact(
    text,
    custom_labels={
        "EMAIL": "***",
        "PHONE": "<hidden-phone>",
    },
)

Notes

• Aadhaar detection can validate candidates using the Verhoeff checksum to reduce false positives.
• Bank account numbers are context-aware and require nearby account-related keywords.
• NER support is optional and degrades gracefully if spaCy or a compatible model is unavailable.
• This library is regex-first and intended for practical text sanitisation workflows, not as a formal compliance guarantee.

Public API

Main exports:

• PIIDetector
• PIIMatch
• indic_pii.detect(...)
• indic_pii.redact(...)
• indic_pii.ner

Python Support

indic-pii supports Python 3.8 and newer.