A Document Layout Analysis Algorithm Based on Lightweight Convolutional Neural Networks
Current document layout analysis methods are often complex,characterized by numerous model parameters and high resource consumption,which presents challenges for deployment on low-power mobile devices.To address this issue,this study proposes a document layout analysis algorithm based on lightweight convolutional neural networks.Initially,a lightweight document feature extraction structure is designed to facilitate implicit feature reuse through structural reparameterization,thereby enhancing the efficiency and speed of document feature extraction.Subsequently,the inclusion of the SPD-Conv module resizes feature maps and expands channels through spatial to depth operations.This enhancement aids in preserving fine-grained information and resolves issues related to image blurriness and the detection of small layout elements.Lastly,a concise feature fusion technique is proposed to optimize the balance between model performance and inference efficiency through model compression.Experimental results demonstrate that the proposed method achieves a mAP@0.5:0.95 score of 93.8％on the PubLayNet dataset using only 1.6 million model parameters.This algorithmic innovation enables exceptional detection accuracy with a reduced parameter count,meeting the requirements for high-performance document layout analysis on mobile devices.
万方数据
维普
