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.

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Dataset Summary

Sub-dataset	Purpose	Size	Format
synthetic_invoices/	OCR fine-tuning (MiniCPM-V)	500 images + annotations	PNG + JSONL
fmcg_catalog.json	Product name normalization (MiniCPM5-1B)	200 SKUs → 2,000 pairs	JSON

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

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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)

Format	Description	Simulated Noise
printed_gst/	GST-compliant printed invoices (A4, 96 DPI)	None
tally_pdf/	Tally ERP-style export layout	Monospace fonts, box borders
handwritten/	Handwritten invoice photos	Gaussian blur, ink texture, skew
whatsapp/	WhatsApp-forwarded invoice screenshots	JPEG compression, timestamp overlay, dark mode

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:

Company	GSTIN State	City
Hindustan Unilever Ltd	Maharashtra	Mumbai
Nestle India Ltd	Delhi	Delhi
Parle Products Pvt Ltd	Maharashtra	Mumbai
Britannia Industries Ltd	Karnataka	Bengaluru
ITC Limited	Tamil Nadu	Chennai
Amul (GCMMF)	Gujarat	Anand
Dabur India Ltd	Uttar Pradesh	Ghaziabad
Marico Limited	Maharashtra	Mumbai
Emami Limited	West Bengal	Kolkata
Godrej Consumer Products	Maharashtra	Mumbai

HF Dataset Schema

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

Column	dtype	Description
image	Image	Embedded invoice image (PIL-compatible)
response	string	Serialized JSON string with extracted invoice fields

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

Split	Examples	Size
Train	450	~169 MB
Test	50	~18.7 MB
Total	500	~188 MB

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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

Category	SKUs	GST Rate	Example Brands
Personal Care	50	18%	Colgate, Dettol, Parachute, Head & Shoulders, Dove
Beverages	45	0–28%	Coca-Cola, Tata Tea, Nescafe, Horlicks, Amul
Home Care	35	18%	Surf Excel, Harpic, Lizol, Vim, Dettol
Biscuits	30	18%	Parle-G, Britannia, ITC Sunfeast, Cadbury Oreo
Dairy	20	0–12%	Amul, Mother Dairy
Atta / Flour	10	5%	Aashirvaad, Annapurna, Fortune, Patanjali
Noodles	10	18%	Maggi, ITC Yippee, Top Ramen, Wai Wai
Total	200	—	10 major Indian FMCG distributors

Augmentation Strategy

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

Technique	Example
Known aliases (hand-curated)	"SURF XL 1K" → Surf Excel Washing Powder 1kg
Uppercase conversion	"AMUL BUTTER 100G" → Amul Butter 100g
Unit abbreviation rules	"500GM" ↔ "500G" ↔ "500GRM"
Product abbreviation rules	"NDL" ↔ Noodles, "TPASTE" ↔ Toothpaste
Common typo injection	"Colgat" ↔ Colgate, "Britania" ↔ Britannia
Random truncation	"AASHIRVAAD ATT" (3-word truncation)
Regional shorthand	"PARLEG" ↔ Parle-G, "H&S" ↔ Head & Shoulders

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

Split	Pairs
Train	1,800
Eval	200
Total	2,000

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Downstream Models

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

Model	Task	HF Hub
MiniCPM-V 4.6	Invoice OCR & JSON extraction	build-small-hackathon/minicpm-v-4-6-indian-invoice-extraction
MiniCPM5-1B	Product name normalization	build-small-hackathon/minicpm5-1b-indian-fmcg-normalizer

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Known Limitations & Biases

Limitation	Impact	Mitigation
Fully synthetic — no real invoice images	May not capture real-world degradation (stains, folds, lighting)	Collect real invoices post-hackathon; add augmentation (blur, noise, shadows)
10 suppliers only	Models may fail on invoices from unrepresented regional distributors	Expand supplier coverage after deployment
English-only product names and labels	Non-English invoices (Hindi, Tamil, Marathi) will fail	Add regional language templates
200 SKUs	Out-of-catalog products will not normalize correctly	Expand catalog to 2,000+ SKUs
Rule-based typo augmentation	Real-world typos and OCR errors may differ from simulated patterns	Collect 200+ real invoice samples for retraining
GST rates hardcoded (0%, 5%, 12%, 18%, 28%)	Uncommon or product-specific rates may be misclassified	Parameterize rate extraction
Brand bias toward premium national brands	May underperform on regional / private-label products	Collect data from regional distributors

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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"])

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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}
}

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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.

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Version: 1.0
Last Updated: June 10, 2026