Image Analytics for Legal Document Review: A Transfer Learning Approach

TL;DR

This paper introduces deep learning applications for legal image review, utilizing transfer learning to improve classification, clustering, and object detection, addressing a gap in multimedia analytics in legal tech.

Contribution

It presents novel transfer learning-based methods for image analytics in legal document review, a first in the legal industry for multimedia content.

Findings

Effective image classification, clustering, and object detection in legal images

Successful application of transfer learning for feature extraction and fine-tuning

Addresses real-world legal review challenges with multimedia data

Abstract

Though technology assisted review in electronic discovery has been focusing on text data, the need of advanced analytics to facilitate reviewing multimedia content is on the rise. In this paper, we present several applications of deep learning in computer vision to Technology Assisted Review of image data in legal industry. These applications include image classification, image clustering, and object detection. We use transfer learning techniques to leverage established pretrained models for feature extraction and fine tuning. These applications are first of their kind in the legal industry for image document review. We demonstrate effectiveness of these applications with solving real world business challenges.