## 2 Related Works
### Dimension Completeness and Redundancy Checks  
Completeness and redundancy checks of annotated dimensions are essential in the design and manufacturing of mechanical products, ensuring no missing or redundant dimensions in the part model. As the part model complexity grows, manual checks become increasingly challenging. Intelligent technologies offer significant potential to improve the efficiency and accuracy of these checks.  
[PERSON] et al. [38] used the \"subtraction method\" to identify redundant dimensional constraints in single-view drawings. [PERSON] et al. [39] introduced a directed graph-based algorithm for dimension completeness checks, using adjacency matrices to detect redundant annotations in dimension chains. [PERSON] [40] optimized redundancy detection by combining the subtraction method with directed graph theory, while [PERSON] [26] transitioned to undirected graphs for redundancy analysis through duplicate node detection in adjacency matrices.  
[PERSON]. [41] proposed a spatial coordinate transformation model and virtual dimension decomposition, supported by a depth-first search algorithm, for completeness checks in multi-view and 3D annotations. [PERSON] et al. [42] introduced equivalent dimension sets to streamline checks for complex 3D models and enhance redundancy detection.  
In 3D MBD (Model-Based Definition) applications, [PERSON] et al. [43] combined dimension chain analysis with completeness checks to address structural complexity. [PERSON] et al. [44] mapped parametric modeling relationships to engineering dimensions, employing [PERSON]'s iteration for simplified completeness analysis.  
Further innovations include those by [PERSON] et al. [45], who used geometric feature constraint models and set constraint propagation to identify redundant and missing annotations, and [PERSON] et al. [46] who employed fuzzy clustering to enforce structural constraints while checking annotation completeness. [PERSON] et al. [11] utilized spatial transformation models and trajectory analysis, respectively, to achieve intelligent, automated checks for 2D and 3D models. [PERSON] et al. [47] proposed a hybrid methodology combining deep learning, clustering, computer vision, and rule-based techniques to address the digitalization of legacy railway interlocking system drawings. Their approach demonstrated robustness in handling noisy, low-quality engineering diagrams by integrating symbol recognition, probabilistic Hough transform for line detection, and graph-based structural representation. [PERSON] et al. [48] proposed an automatic recognition system for digitizing handwritten tabular documents in nuclear power plants. Their approach integrates traditional image processing with neural network methods to denoise scanned documents, extract relevant sections, and apply handwriting recognition to obtain textual and numerical data. The study focuses on detecting tables and cells within scanned handwritten documents, demonstrating high accuracy and practicality in real-world case studies.  
In summary, while advancements in dimension completeness checks have significantly improved efficiency and accuracy, addressing algorithmic efficiency and adapting to the increasing structural complexity of parts remain critical research directions.