Spatial Information Integration in Small Language Models for Document Layout Generation and Classification

TL;DR

This paper introduces a synthetic data generation method for document layout understanding, improving model performance and aiding in scenarios where semi-structured data is scarce.

Contribution

The paper proposes a novel approach to generate synthetic layout data, enhancing training for document understanding models and addressing data scarcity issues.

Findings

Synthetic layout data improves model accuracy.

Proposed method outperforms LayoutTransformer.

Bounding box info enhances text classification.

Abstract

Document layout understanding is a field of study that analyzes the spatial arrangement of information in a document hoping to understand its structure and layout. Models such as LayoutLM (and its subsequent iterations) can understand semi-structured documents with SotA results; however, the lack of open semi-structured data is a limitation in itself. While semi-structured data is common in everyday life (balance sheets, purchase orders, receipts), there is a lack of public datasets for training machine learning models for this type of document. In this investigation we propose a method to generate new, synthetic, layout information that can help overcoming this data shortage. According to our results, the proposed method performs better than LayoutTransformer, another popular layout generation method. We also show that, in some scenarios, text classification can improve when supported by bounding box information.