Multi-Layout Unstructured Invoice Documents Dataset: A Dataset for Template-Free Invoice Processing and Its Evaluation Using AI Approaches

The daily transaction of an organization generates a vast amount of unstructured data such as invoices and purchase orders. Managing and analyzing unstructured data is a costly affair for the organization. Unstructured data has a wealth of hidden valuable information. Extracting such insights automatically from unstructured documents can significantly increase the productivity of an organization. Thus, there is a huge demand to develop a tool that can automate the extraction of key fields from unstructured documents. Researchers have used different approaches for extracting key fields, but the lack of annotated and high-quality datasets is the biggest challenge. Existing work in this area has used standard and custom datasets for extracting key fields from unstructured documents. Still, the existing datasets face some serious challenges, such as poor-quality images, domain-related datasets, and a lack of data validation approaches to evaluate data quality. This work highlights the detailed process flow for end-to-end key fields extraction from unstructured documents. This work presents a high-quality, multi-layout unstructured invoice documents dataset assessed with a statistical data validation technique. The proposed multi-layout unstructured invoice documents dataset is highly diverse in invoice layouts to generalize key field extraction tasks for unstructured documents. The proposed multi-layout unstructured invoice documents dataset is evaluated with various feature extraction techniques such as Glove, Word2Vec, FastText, and AI approaches such as BiLSTM and BiLSTM-CRF. We also present the comparative analysis of feature extraction techniques and AI approaches on the proposed multi-layout unstructured invoice document dataset. We attained the best results with BiLSTM-CRF model.