HSCA-Net: A Hybrid Spatial-Channel Attention Network in Multi-Scale Feature Pyramid for Document Layout Analysis

Abstract

Document images often contain various page components and complex logical structures, which makes document layout analysis task challenging. For most deep learning based document layout analysis methods, convolutional neural networks (CNNs) are adopted as the image feature extraction networks. In this paper, a hybrid spatial-channel attention network (HSCA-Net) is proposed to improve feature extraction capability by exerting attention mechanism to explore more salient properties within document pages. The HSCA-Net contains two modules: spatial attention module (SAM) and channel attention module (CAM). They are embedded in the multi-scale feature network by lateral attention connection. SAM extracts contextual information with learning offset in spatial dimension and CAM performs feature recalibration by focusing more on feature channels with important contents. The lateral attention connection is to incorporate SAM and CAM into multi-scale feature pyramid network and retain more of the original feature information. The effectiveness and adaptability of HSCA-Net are evaluated through multiple experiments on publicly available datasets PubLayNet, ICDAR-POD and Article Regions. The mAP on these datasets is as high as 0.940,0.939 and 0.967 respectively, which demonstrate that our HSCA-Net achieves competitive results on document layout analysis task. Text.

Segmentation-free Recognition Algorithm Based on Deep Learning for Handwritten Text Image

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