build-small-hackathon/kirana-invoice-train-data

Kirana Invoice Training Data — Indian FMCG

Training dataset for the Kirana Detective project — an AI pipeline that audits distributor invoices for Indian kirana (grocery) stores. The repository contains two distinct sub-datasets used to fine-tune two separate models.

Dataset Summary

Both sub-datasets are fully synthetic — generated programmatically from a hand-curated SKU catalog. No real customer or business data is included.

Sub-dataset 1: Synthetic Invoice Images

Overview

500 invoice images rendered in pure Python (Pillow) across four realistic formats. Designed to teach MiniCPM-V to extract structured JSON from invoice photos regardless of format, quality, or layout.

Invoice Formats (125 images each)

Invoice Contents

Each invoice contains:

• —5–14 line items randomly sampled from the 200-SKU FMCG catalog
• —Supplier (1 of 10 major Indian FMCG distributors) with GSTIN
• —Buyer (1 of 8 kirana store archetypes) with GSTIN
• —Invoice number in formats: INV/2024-25/XXXXX, TAX/..., GST/..., BILL/...
• —Date between April 2024 and March 2025
• —Pricing in range ₹8–₹480 per item (15% anomaly rate for training diversity)
• —GST calculations at 0%, 5%, 12%, 18%, or 28% depending on product category

Suppliers represented:

HF Dataset Schema

The dataset on HuggingFace Hub is stored as Parquet with two columns:

The response string follows this structure:

{
  "supplier": "Nestle India Ltd",
  "gstin_supplier": "07AAACN0032R1ZX",
  "buyer": "Ravi Provision Store",
  "gstin_buyer": "33AABCR5678K1ZQ",
  "invoice_number": "INV/2024-25/04821",
  "date": "2024-09-14",
  "line_items": [
    {
      "raw_name": "MAGGI NDL 70GM",
      "quantity": 12,
      "unit_price": 45.50,
      "gst_rate": 18,
      "total": 546.00
    }
  ],
  "subtotal": 3840.00,
  "gst_total": 691.20,
  "invoice_total": 4531.20
}

Data Splits

Sub-dataset 2: FMCG Product Name Normalization Pairs

Overview

A structured catalog of 200 Indian FMCG SKUs with known abbreviations and aliases, used to generate 2,000 synthetic (raw, canonical) training pairs for MiniCPM5-1B.

Catalog Structure (fmcg_catalog.json)

Each entry:

{
  "product_id": "maggi_masala_70g",
  "canonical_name": "Nestle Maggi Masala Noodles 70g",
  "hsn_code": "1902",
  "gst_rate": 18,
  "category": "noodles",
  "brand": "Nestle",
  "common_aliases": [
    "MAGGI 70G",
    "MAGGI NDL 70",
    "MAGGI MSL 70G",
    "MAGGI MASALA 70",
    "MGI 70G",
    "MAGGI 70GM"
  ]
}

SKU Breakdown by Category

Augmentation Strategy

Each canonical SKU name is transformed into realistic raw invoice variants using rule-based augmentation:

Normalization Pair Format

Each training sample follows a chat template:

{
  "messages": [
    {
      "role": "system",
      "content": "You are an Indian FMCG product name normalizer. Given a raw product name from a distributor invoice, return ONLY the canonical product name. No explanation, no punctuation — just the canonical name."
    },
    {
      "role": "user",
      "content": "Invoice product name: \"MAGGI NDL 70GM\""
    },
    {
      "role": "assistant",
      "content": "Nestle Maggi Masala Noodles 70g"
    }
  ]
}

Data Splits

Downstream Models

This dataset is used to fine-tune two models in the Kirana Detective pipeline:

Known Limitations & Biases

Load from HuggingFace Hub

from datasets import load_dataset
import json

ds = load_dataset("build-small-hackathon/kirana-invoice-train-data")

sample = ds["train"][0]
image = sample["image"]                    # PIL Image — ready for model input
data  = json.loads(sample["response"])     # parse the JSON string

print(data["supplier"])
print(data["line_items"])

Citation

@misc{kirana_invoice_train_data_2026,
  author    = {Syed Naazim hussain},
  title     = {Kirana Invoice Training Data: Synthetic Indian FMCG Invoices for OCR and Product Normalization},
  year      = {2026},
  publisher = {HuggingFace},
  howpublished = {\url{https://huggingface.co/datasets/build-small-hackathon/kirana-invoice-train-data}},
  note      = {Part of the Kirana Detective project}
}

License

CC0 1.0 Universal (Public Domain Dedication) All data in this repository — synthetic invoice images, annotations, and the SKU catalog — is released to the public domain. No attribution required.

The generation scripts (generate_invoices.py, build_catalog.py) are licensed under MIT.

Version: 1.0 Last Updated: June 10, 2026