π AI Powered β’ π GIS Enabled β’ π€ Machine Learning β’ π Published Research β’ ποΈ Smart Governance
Digi-Gram is an AI-powered digital governance platform developed to modernize the functioning of Gram Panchayats through intelligent automation, digital services, and real-time analytics.
The platform enables citizens and administrators to efficiently manage complaints, government schemes, tax payments, certificates, village census, GIS-based assets, and public services from a single unified system.
By integrating Artificial Intelligence, Machine Learning, OCR, NLP, GIS, and Full-Stack Web Technologies, Digi-Gram aims to improve transparency, reduce manual work, and deliver faster, smarter, and citizen-centric governance.
- π Online Complaint Registration
- π’ Government Scheme Information
- π Online Certificate Requests
- π³ Digital Tax Payment
- π± Mobile-Friendly Interface
- π§ AI Complaint Prioritization
- π¬ Intelligent Chatbot Assistance
- π OCR-Based Document Processing
- π·οΈ Automatic Complaint Categorization
- π Predictive Analytics & Insights
- π¨βπΌ Secure Admin Dashboard
- π Complaint Analytics
- πΊοΈ GIS-Based Village Asset Mapping
- π₯ User & Staff Management
- π Real-Time Reports
- π Notifications & Status Tracking
- π Role-Based Authentication
- π‘οΈ Secure REST APIs
- π Encrypted User Data
- β Input Validation
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React.js HTML5 CSS3 JavaScript Bootstrap |
Java Spring Boot Spring Security REST APIs Hibernate (JPA) |
MySQL Firebase MongoDB Cloudinary |
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Python BERT NLP OCR OpenAI API Hugging Face |
QGIS Leaflet.js OpenStreetMap GeoJSON |
Git GitHub Docker Render Postman VS Code |
The Digi-Gram platform follows a modular Full-Stack architecture where citizens interact with a React-based frontend, which communicates securely with Spring Boot REST APIs. The backend integrates AI modules for complaint prioritization, OCR, NLP, chatbot services, GIS mapping, and analytics while storing structured and semi-structured data using MySQL, Firebase, and MongoDB.
Citizen Complaint
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βΌ
Complaint Registration
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βΌ
Text Preprocessing
(Lowercase β’ Stopword Removal β’ Tokenization)
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βΌ
TF-IDF Feature Extraction
(3000 Features β’ N-grams 1β3)
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βΌ
Ensemble Machine Learning Model
(Logistic Regression + Random Forest +
Gradient Boosting + SVM)
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βΌ
Soft Voting Mechanism
β
βΌ
Priority Classification
(HIGH β’ MEDIUM β’ LOW)
β
βΌ
Confidence Score Generation
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βΌ
Admin Dashboard & Analytics
The complaint text submitted by the citizen undergoes preprocessing, including text normalization, stopword removal, and tokenization. The processed text is transformed into numerical feature vectors using the TF-IDF Vectorizer. These features are evaluated by an ensemble of machine learning models consisting of Logistic Regression, Random Forest, Gradient Boosting, and Support Vector Machine (SVM). The final priority is determined using a Soft Voting Classifier, which predicts whether the complaint should be classified as High, Medium, or Low priority along with a confidence score. The prediction is then displayed on the administrative dashboard for efficient complaint management.
Digi-Gram
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βββ assets/
β
βββ backend/
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βββ frontend/
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βββ ml-models/
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βββ docs/
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βββ README.md
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βββ LICENSE
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βββ .gitignore
π The research work behind Digi-Gram has been successfully published in the International Journal of Computer Techniques (IJCT).
| Field | Details |
|---|---|
| Paper Title | DigiGram: An AI-Powered Real-Time Digital Governance Platform for Rural Panchayats in India |
| Journal | International Journal of Computer Techniques (IJCT) |
| ISSN | 2394-2231 |
| Volume | 13 |
| Issue | 3 |
| Year | 2026 |
| Paper ID | IJCT-V13I3P1 |
| Author | Durvesh Rajesh Nayak |
The successful completion and publication of this research validates the technical contribution of Digi-Gram in the field of Artificial Intelligence, Smart Governance, and Digital Transformation for Rural India.
The certificate is included in the docs/ directory for reference.
## π Project Guide
Supriya Chougule
Project Mentor
|
Full Stack Developer AI Integration β’ Backend β’ System Architecture |
Full Stack Developer Frontend β’ Backend β’ Application Development |
Full Stack Developer Frontend β’ Backend β’ System Development |
Made with β€οΈ by Durvesh Rajesh Nayak, Harshil Hulyalkar, and Parthamesh Mulik
β If you found this project interesting, consider giving it a star.
--- # π LicenseThis project is intended for academic, research, and educational purposes.
Β© 2026 Durvesh Rajesh Nayak. All Rights Reserved.


