DoTNet: Document Layout Classification Using TextureBased CNN Document Layout Analysis (DLA) is a segmentation process that decomposes a scanned document image into its blocks of interest and classifies them. DLA is essential in a large number of applications, such as Information Retrieval, Machine Translation, Optical Character Recognition (OCR) systems, and structured data extraction from documents. However, identification of document blocks in DLA is challenging due to variations of block locations, interand intraclass variability, and background noises. In this paper, we propose a novel texturebased convolutional neural network for document layout analysis, called DoTNet. DoTNet is a multiclass classifier that can effectively identify document component blocks such as text, image, table, mathematical expression, and linediagram, whereas most related methods have focused on the text vs. nontext block classification problem. DoTNet can capture textural variations among the multiclass regions of documents. Our proposed method DoTNet achieved promising results outperforming stateoftheart document layout classifiers on accuracy, F1 score, and AUC. The opensource code of DoTNet is available at https:github.comdataxlabDoTNet.

PubLayNet: Largest Dataset Ever for Document Layout Analysis Recognizing the layout of unstructured digital documents is an important step when parsing the documents into structured machinereadable format for downstream applications. Deep neural networks that are developed for computer vision have been proven to be an effective method to analyze layout of document images. However, document layout datasets that are currently publicly available are several magnitudes smaller than established computing vision datasets. Models have to be trained by transfer learning from a base model that is pretrained on a traditional computer vision dataset. In this paper, we develop the PubLayNet dataset for document layout analysis by automatically matching the XML representations and the content of over 1 million PDF articles that are publicly available on PubMed Central. The size of the dataset is comparable to established computer vision datasets, containing over 360 thousand document images, where typical document layout elements are annotated. The experiments demonstrate that deep neural networks trained on PubLayNet accurately recognize the layout of scientific articles. The pretrained models are also a more effective base mode for transfer learning on a different document domain. We release the dataset (https:github.comibmaurnlpPubLayNet) to support development and evaluation of more advanced models for document layout analysis.