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Memory-efficient document layout analysis method using LD-net
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PRE
AI
DOI：10.1007/s11042-022-12497-9.png)
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摘要
En 中文
Document layout analysis is a critical step in optical character recognition. Traditional handcraft feature-based methods cannot handle various formats to obtain high accuracy. Although, deep-learning based methods obtain satisfactory accuracy, they are not memory-efficient for low-memory devices such as mobile phone. To alleviate such problems, a memory-efficient approach to layout analysis with the Lightweight Dilated Network (LD-Net) is proposed in this study. The initial document page image is segmented into blocks of content via Otsu algorithm and RLSA. Each block is sent into the LD-Net to classify them into four common different classes, figure, table, text, and formula. The main structure of the LD-Net is a shallow network, which performs better than deeper network for layout analysis task. Each convolution layer is composed of depthwise separable convolution and residual structure. In addition, the dilated convolution is also employed in the LD-Net to improve the accuracy of detection results. Experimental results based on benchmarks show that the proposed approach gets better performance in accuracy and memory occupied. The accuracy of the model on ICDAR dataset is 95.7% and the memory of the model occupies 39.7MB, which outperforms the existing methods.
Keyword:
Optical character recognition
Document layout analysis
Image processing
Lightweight dilated network
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3
论文数:
2.0W
被引数:
3.2W
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