Lawsuits Document Images Processing Classification

Abstract

Natural Language Processing techniques usually fail to classify low quality lawsuit document images produced by a flatbed scanner or fax machine or even captured by mobile devices, such as smartphones or tablets. As the courts of justice have many lawsuits, the manual detection of classification errors is unfeasible, favouring fraud, such as using the same payment receipt for more than one fee. An alternative to classifying low-quality document images is visual-based methods, which extract features from the images. This article proposes classification models for lawsuit document image processing using transfer learning to train Convolutional Neural Networks most quickly and obtain good results even in smaller databases. We validated our proposal using a TJSP dataset composed of 2,136 unrecognized document images by Natural Language Processing techniques and reached an accuracy above 80% in the proposed models.