mlsanityData to 14 Oct 2026

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

arXiv 2502.16541 ↗First posted 23 Feb 2025cs.CV cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 21 of its 42 references that are in the map. First posted on arXiv 23 Feb 2025.

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21 of 42 references.

References · 21

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  12. 2021LayoutReader: Pre-training of Text and Layout for Reading Order Detectioncited by 80
9 more
  1. 2024D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinementcited by 74
  2. 2023Vision Grid Transformer for Document Layout Analysiscited by 47
  3. 2023M6Doc: A Large-Scale Multi-Format, Multi-Type, Multi-Layout, Multi-Language, Multi-Annotation Category Dataset for Modern Document Layout Analysiscited by 46
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  9. 2023DSG: An End-to-End Document Structure Generatorcited by 7

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  2. 2024DocEDA: Automated Extraction and Design of Analog Circuits from Documents with Large Language Model
  3. 2025D2S-FLOW: Automated Parameter Extraction from Datasheets for SPICE Model Generation Using Large Language Models
  4. 2022PP-StructureV2: A Stronger Document Analysis System
  5. 2024LayoutLLM: Layout Instruction Tuning with Large Language Models for Document Understanding
  6. 2025LED Benchmark: Diagnosing Structural Layout Errors for Document Layout Analysis
  7. 2026LED: A Benchmark for Evaluating Layout Error Detection in Document Analysis
  8. 2026OmniSch: A Multimodal PCB Schematic Benchmark For Structured Diagram Visual Reasoning