Problem
Research questions and friction points this paper is trying to address.
PCB engineering drawings
region localization
specialized recognizers
full-page parsing
content recovery
Learning to Ground Before Reading: Unified PCB Engineering Drawing Parsing with Compact Vision-Language Models
🤖 AI Summary
研究通过训练紧凑的视觉-语言模型来解析PCB工程图纸，无需使用检测器或裁剪解析器，解决了现有方法中因区域遗漏导致的问题。
📝 Abstract
PCB engineering drawings mix sparse graphics, dense tables, and text whose meaning depends on page position. Localizing the regions and sending crops to specialized recognizers are determined as the methods for most parsers, so missed regions cannot be recovered downstream. We train a compact VLM to read the full page and get a sequence of region classes, normalized boxes, and text or HTML content. Bounding boxes are converted to coordinate tokens for supervision. Inference uses no detector or crop parser. The joint target is difficult to optimize because class and box tokens are sparse relative to the much longer content sequences. Our localization-first curriculum learns the class-box format before adding content targets with content-aware resampling. On the fixed validation split of the Engineering Drawing Dataset (ED dataset), Localization-First improves strict localization F1 by 0.0955 over joint training (paired image-bootstrap 95% interval: [0.0350, 0.1572]). G-Unified has the lowest NED, highest cell F1, and only nonzero exact-match score. It provides a detector-free baseline for full-page PCB drawing parsing.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Compact Vision-Language Models
Localization-First Curriculum
Detector-Free Parsing
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