用知识蒸馏提升电子器件文档版面分析效率
EDocNet: Efficient Datasheet Layout Analysis Based on Focus and Global Knowledge Distillation
- 结合焦点与全局知识蒸馏，优化版面分析模型
- 在21类电子器件文档中实现更高准确率与召回率
- 适合需要快速解析技术文档的工程师使用
电路设计中，工程师需查阅大量文档获取元器件信息，效率低下且工作量大。当前文档版面分析模型适用于多种文档类型，但不适用于电子器件文档。本文提出EDocNet，基于自建电子器件文档数据集，采用焦点与全局知识蒸馏训练方法，实现针对电子器件文档的版面分析。该模型可将文档内容划分为21个类别，在平均准确率和平均召回率上表现更优，并显著提升模型推理速度。
原文摘要 · Abstract (English)
When designing circuits, engineers obtain the information of electronic devices by browsing a large number of documents, which is low efficiency and heavy workload. The use of artificial intelligence technology to automatically parse documents can greatly improve the efficiency of engineers. However, the current document layout analysis model is aimed at various types of documents and is not suitable for electronic device documents. This paper proposes to use EDocNet to realize the document layout analysis function for document analysis, and use the electronic device document data set created by myself for training. The training method adopts the focus and global knowledge distillation method, and a model suitable for electronic device documents is obtained, which can divide the contents of electronic device documents into 21 categories. It has better average accuracy and average recall rate. It also greatly improves the speed of model checking.
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