Doc-DINO: A Transformer Model for Complex Logical Document Layout Analysis

Document layout analysis is an indispensable part of document information processing. It can be applied to various tasks such as document retrieval, machine translation, document information retrieval, and structured data extraction from documents. However, most publicly available datasets in the field of layout analysis primarily consist of documents with a single layout type, are in the English language, and are limited to PDF documents. In this paper, we propose the Doc-DINO model for analyzing complex logical document layouts using a dataset that includes multiple formats, types, and a wider range of categories. Firstly, Aiming to learn more abstract and advanced representations by fusing multi-scale features, the Cross-Scale Convolution Fusion (CSCF) is proposed as the neck of the model. Secondly, the Fully Convolutional Multi-Core Self-Attention (FCMS) Encoder is presented, which includes convolutional attention and convolutional feedforward networks to better capture relationships between inputs and enhance the model’s expressive power. The model achieves a mean Average Precision (mAP) of 65.7 on the complex document layout analysis dataset M6Doc and 64.2 on SCUT-CAB, setting a new state-of-the-art performance for these datasets.