EDocNet: Efficient Datasheet Layout Analysis Based on Focus and Global Knowledge Distillation

TL;DR

EDocNet is a specialized AI model designed for efficient layout analysis of electronic device datasheets, improving accuracy and speed by using focus and global knowledge distillation on a custom dataset.

Contribution

The paper introduces EDocNet, a novel model tailored for electronic device datasheet layout analysis, utilizing a new training method with focus and global knowledge distillation.

Findings

Achieves higher accuracy and recall rate on electronic device documents.

Significantly improves model checking speed.

Successfully classifies 21 categories in datasheet layouts.

Abstract

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.