Overview
A Smart India Hackathon 2024 concept for problem statement 1711, “Enhancing Rail Madad with AI-powered Complaint Management”.
The proposal uses OCR and deep learning to understand complaints submitted as text, images, or video, in multiple languages, on scalable cloud infrastructure. It is presented here as a concept — it was not deployed as a production system.
The challenge
Smart India Hackathon 2024 problem statement 1711 asked teams to enhance Rail Madad, Indian Railways' complaint platform, with AI-powered complaint management. Complaints arrive in many forms — text, photos, and videos — and in many languages, which makes them slow to understand and route by hand.
Status: this is a hackathon concept. The approach below describes a proposed design; it was not deployed as a production system.
Proposed architecture
A concept design — not a deployed system.
4 layers · select a layer for details
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Intake Multimodal complaints
Text, images, and video submitted by passengers.
- Text
- Images
- Video
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Extraction OCR
Extract text that appears in submitted images.
- OCR
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Understanding AI / deep learning models
Understand and categorize complaint content across languages and formats.
- Deep learning
- Multilingual NLP
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Platform Scalable cloud services
Infrastructure sized for varying complaint volumes.
- Cloud
Proposed approach
- Multimodal intake. Accept complaints as text, images, or video.
- OCR to extract text from images, such as details visible in a photo.
- AI / deep learning to understand and categorize complaint content across these formats.
- Multilingual support so passengers can complain in the language they're comfortable with.
- Cloud scalability so the system can handle varying complaint volumes.
What I took from it
Designing for a system at national scale — many languages, many input formats, and high volume — is a very different exercise from building a single application. The hackathon was a useful introduction to thinking about AI components as parts of a larger service rather than as standalone models.
