# Image Analytics for Legal Document Review: A Transfer Learning Approach

Source: [Image Analytics for Legal Document Review: A Transfer Learning Approach](<https://www.sota2.com/research/paper/68fa66d11e209131cea1298f>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:59:21.344Z

Authors: Nathaniel Huber-Fliflet, Fusheng Wei, Haozhen Zhao, Han Qin, Shi Ye, Amy Tsang

Published: 2019-12-19T19:11:15.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1912.12169>)

[PDF](<https://arxiv.org/pdf/1912.12169.pdf>)