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SIH Buddyby Ganeev Singh
Dev
All problem statements
SIH26034Strong pickacceptance 4/5

Software System to check compliance of Packaged Commodities under Legal Metrology(Packaged Commodities) Rules, 2011 by scanning products, images and labels.

Ministry of Consumer Affairs, Food & Public Distribution · Agriculture, FoodTech & Rural Development · Software

Statutory rules define correctness for you, training data is a supermarket away and any judge can test it with the packet in their hand — just do not dodge the font-size-in-millimetres problem, because that is the only part of this that is actually hard and therefore the only part that distinguishes you.

Open dataset ↗

Data: Legal Metrology Act and Rules reference: https://consumeraffairs.gov.in/pages/legal-metrology-act — together with the Legal Metrology (Packaged Commodities) Rules, 2011

What it actually is

Every packaged product sold in India must legally print certain things on it — who made it, how much is inside, the maximum price, when it was packed — in a prescribed format and a minimum letter size. Inspectors currently check this by hand, one packet at a time. The ask is software that photographs a label and reports which of those legal requirements it fails.

What to build

A label compliance scanner where an inspector photographs a package and receives a rule-by-rule verdict: text detection and OCR extracts the printed declarations, a matcher identifies which mandatory declarations under the 2011 Rules are present and which are missing, a geometric analysis converts detected character heights into millimetres using package dimensions or a reference object so the prescribed minimum font size can actually be checked rather than estimated, format validation catches malformed MRP and net quantity declarations, and the result is written into a compliance report with the offending regions annotated on the photograph, filed into a searchable product repository with inspection history and an enforcement dashboard.

Smallest thing that wins the room

Let a judge hold up a snack packet from their own bag, photograph it, and get back a report naming the specific declaration that is missing or under-size with that region circled on the image.

How crowded this one gets

A guess, projected from the 2025 statements — the last year where both the submission counts and the winners were published.

Moderate120–290 teams expectedroughly 1 in 105–242 wins it

Quieter than 52% of the 226 · #109 of 226 by expected field

A normal-sized field. Your idea has to be good, not miraculous.

Why: central ministry statements sat below the average.

This is a guess, not a fact

Nobody has published 2026’s numbers yet. This is an analysed estimate from last year’s pattern, so please do not take it as the truth — check the live counter on the SIH portal before you decide anything. The range covers the middle half of likely outcomes, so one statement in two lands outside it. Entry closes at 500 ideas per statement, so no range goes past that — a statement that reaches the cap fills and shuts rather than drawing an unlimited crowd. The model reads only three things a team can see before choosing — software or hardware, the theme, and what kind of body posted it — and those explain about a quarter of the variation in last year’s field sizes (R² 0.25 on held-out statements). Trust the band more than the number, and the ordering more than either. It cannot see how good your idea is, which is the part that actually decides it.

The scores

The number is the shorthand. The line under it is the reason.

What you will be writing

  • DBNet / CRAFT scene text detection on packaging
  • PaddleOCR multilingual label extraction
  • homography-based pixel-to-millimetre font height estimation
  • rule engine encoding Packaged Commodities Rules 2011
  • annotated PDF violation report generation
  • PostgreSQL product and inspection repository
  • Legal metrology enforcement
  • Document and scene text OCR
  • Consumer protection

Prior art to read before you start

packaged label declaration compliance checking · scene text extraction from product packaging · rule-based regulatory validation

Analysed by Claude Opus. Every score above is a judgment call with its reasoning attached — kindly cross-check this against the official statement on the SIH portal before your team commits to it.