Convolutional Neural Networks can achieve binary bail judgement classification
This work addresses the lack of ML applications in the legal domain for lower courts and regional languages in India, though it is incremental as it builds on existing methods.
The paper tackled the problem of bail judgment classification using a Convolutional Neural Network (CNN) on Hindi legal documents from lower courts in India, achieving an overall accuracy of 93%, which improves upon a prior benchmark.
There is an evident lack of implementation of Machine Learning (ML) in the legal domain in India, and any research that does take place in this domain is usually based on data from the higher courts of law and works with English data. The lower courts and data from the different regional languages of India are often overlooked. In this paper, we deploy a Convolutional Neural Network (CNN) architecture on a corpus of Hindi legal documents. We perform a bail Prediction task with the help of a CNN model and achieve an overall accuracy of 93\% which is an improvement on the benchmark accuracy, set by Kapoor et al. (2022), albeit in data from 20 districts of the Indian state of Uttar Pradesh.