An Automated Software System for Compliance Verification of Packaged Commodities under the Legal Metrology (Packaged Commodities) Rules, 2011, through Image and Label Analysis

Packaged commodities sold across retail, supermarket and e-commerce channels in India are legally required to display mandatory declarations — such as net quantity, Maximum Retail Price (MRP), manufacturer details, date of packing, and consumer-care information — under the Legal Metrology (Packaged Commodities) Rules, 2011. In practice, manual verification of these declarations by enforcement officials is slow, resource-intensive and prone to oversight given the sheer volume and variety of products in the market. This paper presents the design and implementation of a software system that automates this verification process by scanning product label images and validating the extracted content against the rules. The proposed pipeline combines a lightweight scene-text detector, an OCR-based text extraction module, an NLP-based field parser, and a rule-based compliance engine, and exposes its output through a report-generation module and an enforcement dashboard. The system was evaluated on a curated set of packaged-product label images across multiple product categories and shows that automated scanning can detect missing or non-compliant declarations with substantially less time and effort than manual inspection, while producing an auditable digital record of every compliance check performed.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-18
DOI
https://doi.org/10.5281/zenodo.23453877
Primary Topic
Handwritten Text Recognition Techniques
Type
preprint
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preprint

An Automated Software System for Compliance Verification of Packaged Commodities under the Legal Metrology (Packaged Commodities) Rules, 2011, through Image and Label Analysis

Parag Jadhav, Sanjay Kumawat, Sneha Annappanavar, Siddhesh Rajgure
Zenodo (CERN European Organization for Nuclear Research)
Handwritten Text Recognition Techniques
preprint

An Automated Software System for Compliance Verification of Packaged Commodities under the Legal Metrology (Packaged Commodities) Rules, 2011, through Image and Label Analysis

Parag Jadhav, Sanjay Kumawat, Sneha Annappanavar, Siddhesh Rajgure
preprint en

Abstract

Packaged commodities sold across retail, supermarket and e-commerce channels in India are legally required to display mandatory declarations — such as net quantity, Maximum Retail Price (MRP), manufacturer details, date of packing, and consumer-care information — under the Legal Metrology (Packaged Commodities) Rules, 2011. In practice, manual verification of these declarations by enforcement officials is slow, resource-intensive and prone to oversight given the sheer volume and variety of products in the market. This paper presents the design and implementation of a software system that automates this verification process by scanning product label images and validating the extracted content against the rules. The proposed pipeline combines a lightweight scene-text detector, an OCR-based text extraction module, an NLP-based field parser, and a rule-based compliance engine, and exposes its output through a report-generation module and an enforcement dashboard. The system was evaluated on a curated set of packaged-product label images across multiple product categories and shows that automated scanning can detect missing or non-compliant declarations with substantially less time and effort than manual inspection, while producing an auditable digital record of every compliance check performed.

Zenodo (CERN European Organization for Nuclear Research)
Vidyalankar Institute of Technology (IN)
Handwritten Text Recognition Techniques
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