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.
@article{arxiv.1912.12169,
title = {Image Analytics for Legal Document Review: A Transfer Learning Approach},
author = {Nathaniel Huber-Fliflet and Fusheng Wei and Haozhen Zhao and Han Qin and Shi Ye and Amy Tsang},
journal= {arXiv preprint arXiv:1912.12169},
year = {2019}
}
Comments
2019 IEEE International Conference on Big Data (Big Data)