Summary of Llm For Barcodes: Generating Diverse Synthetic Data For Identity Documents, by Hitesh Laxmichand Patel et al.
LLM for Barcodes: Generating Diverse Synthetic Data for Identity Documents
by Hitesh Laxmichand Patel, Amit Agarwal, Bhargava Kumar, Karan Gupta, Priyaranjan Pattnayak
First submitted to arxiv on: 22 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a novel approach to synthetic data generation for accurate barcode detection and decoding in identity documents. Using Large Language Models (LLMs), the method creates contextually rich and realistic data without relying on predefined templates or fields. This is particularly important for applications like security, healthcare, and education where reliable data extraction and verification are crucial. The generated data is then encoded into barcodes and overlaid on templates for various documents such as Driver’s licenses, Insurance cards, and Student IDs. The proposed approach simplifies the process of dataset creation, eliminating the need for extensive domain knowledge or predefined fields. Compared to traditional methods like Faker, the LLM-generated data demonstrates greater diversity and contextual relevance, leading to improved performance in barcode detection models. This scalable, privacy-first solution is a significant step forward in advancing machine learning for automated document processing and identity verification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates synthetic data using Large Language Models (LLMs) to generate realistic documents like Driver’s licenses, Insurance cards, and Student IDs. The goal is to make barcode detection and decoding more accurate for security, healthcare, and education applications. Traditional methods rely on predefined templates, but this approach uses LLMs to create complex and varied data. This makes it better suited for real-world identity documents. The generated data is then used to test and improve barcode detection models. The paper solves a big problem in machine learning – creating realistic datasets without compromising privacy. This breakthrough could help many areas where accurate document processing is important. |
Keywords
» Artificial intelligence » Machine learning » Synthetic data
