HarshilDaGoat/gem-certificate-extractor

GeM certificate field extractor

Token-classification model that reads the OCR text of Indian business certificates submitted with Government e-Marketplace (GeM) bids and extracts the fields a compliance officer cross-checks: GSTIN, PAN, Udyam number, CIN, EPFO establishment code, DPIIT number, legal and trade name, activity, enterprise category, certified local-content % and validity-until date.

Part of an automated bid-compliance platform; it replaces a regex parser in the document stage, and its output is cross-verified against government portal data by an auditable rule engine.

Intended use

Input: plain text from a certificate (PDF text layer or OCR). Output: one value per field, with a confidence. Covered document types: GST registration certificate (REG-06), Udyam certificate, Make in India local-content self-certificate, PAN card, EPFO compliance certificate, DPIIT startup recognition, OEM authorization letter, CA turnover certificate, NSIC registration.

It is an extraction aid, not a verifier: a correctly extracted GSTIN says nothing about whether that GSTIN is active — that comes from the GST portal.

Training data

Generated by ml/extraction/generate.py: 54,000 train, 3,000 validation, 3,000 test examples.

The generator deliberately includes look-alikes a regex gets wrong: the OEM's own GSTIN beside the bidder's in authorization letters, a "minimum local content of N%" threshold restated before the certified figure, validity start dates, PANs embedded in GSTINs, past-year Udyam classifications, and UDIN/FRN/ESIC numbers — plus OCR-style character confusions (O/0, S/5, I/1) and merged lines.

All training data is synthetic. Identifiers follow real formats (GSTINs carry a valid check character) but correspond to no real entity. Accuracy on real, scanned certificates with layouts unlike the templates will be lower than the figures below; evaluate on your own documents first.

Evaluation (held-out synthetic test set)

Entity-level, exact span match:

Field	Precision	Recall	F1	Support
ACTIVITY	100.0%	100.0%	100.0%	902
CATEGORY	100.0%	100.0%	100.0%	902
CIN	100.0%	100.0%	100.0%	124
DPIIT	100.0%	100.0%	100.0%	312
EPFO	100.0%	100.0%	100.0%	311
GSTIN	100.0%	100.0%	100.0%	1174
LEGAL_NAME	100.0%	100.0%	100.0%	3000
LOCAL_CONTENT	100.0%	99.7%	99.8%	296
PAN	100.0%	100.0%	100.0%	593
TRADE_NAME	100.0%	100.0%	100.0%	296
UDYAM	100.0%	100.0%	100.0%	902
VALID_UNTIL	100.0%	100.0%	100.0%	789
All (micro)	100.0%	100.0%	100.0%	9601

Usage

from transformers import pipeline
extract = pipeline("token-classification", model="HarshilDaGoat/gem-certificate-extractor", aggregation_strategy="simple")
extract("Registration Number: 27AABCN1234A1Z5\nLegal Name of Business: Nova Electro Systems Private Limited")

For field values normalised exactly as the platform uses them (dates as ISO, local content as a number, one value per field), use ml/extraction/infer.py from the project repository.