Verdicteye - Predict Court Judgements

India's legal system generates vast amounts of textual data, making it challenging to efficiently identify relevant Indian Penal Code (IPC) sections. Manual analysis is timeconsuming, labor-intensive, and prone to errors, necessitating an automated solution. VerdictEye utilizes NLP and machine...

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Bibliographic Details
Published in:2025 3rd International Conference on Communication, Security, and Artificial Intelligence (ICCSAI) Vol. 3; pp. 2059 - 2064
Main Authors: Singh, Abhishek Kumar, Sharma, Aditi, Verma, Aishwarya, Srivastava, Ayush, Shukla, Pallavi
Format: Conference Proceeding
Language:English
Published: IEEE 04.04.2025
Subjects:
Accuracy
Codes
IPC Prediction
Law
Legal Technology
Logistic regression
Machine Learning
Manuals
Natural Language Processing
Real-time systems
Security
Support vector machines
Text categorization
Tokenization
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Summary:India's legal system generates vast amounts of textual data, making it challenging to efficiently identify relevant Indian Penal Code (IPC) sections. Manual analysis is timeconsuming, labor-intensive, and prone to errors, necessitating an automated solution. VerdictEye utilizes NLP and machine learning to analyze incident descriptions and accurately predict relevant IPC sections. The system processes raw legal text using techniques like tokenization, stop-word removal, and TF-IDF vectorization to enhance structure and readability. Various machine learning models, including Logistic Regression, Random Forest, and Support Vector Machines (SVM), were trained for classification. Among them, Logistic Regression achieved the best accuracy and consistency, making it the preferred model for legal text classification. With a React-based interface, VerdictEye ensures real-time data input and predictions, enhancing accessibility for legal professionals. Future enhancements include incorporating ad- vanced NLP models, regional language support, and cloudbased deployment, positioning VerdictEye as a transformative tool for modernizing India's legal research process.
DOI:10.1109/ICCSAI64074.2025.11063847