{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T02:39:23Z","timestamp":1769740763921,"version":"3.49.0"},"reference-count":29,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T00:00:00Z","timestamp":1769644800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Title block compliance checking requires interpreting irregular tabular layouts and reporting structural inconsistencies, not only extracting metadata. This paper introduces a user-in-the-loop, template-based method that leverages a graphical annotation workflow to encode title block structure as a hierarchical annotation graph combining detected primitives (cells\/text) with user-defined semantic entities (key\u2013value pairs, tables, headers). The resulting template is matched onto target title blocks using relative positional constraints and category-specific rules that distinguish acceptable variability from non-compliance (e.g., variable-size tables versus missing fields). The system outputs extracted key\u2013value information and localized warning logs for end-user correction. On a real industrial example from the nuclear domain, the approach achieves 98\u201399% compliant annotation matching and 84% accuracy in flagging structural\/content deviations, while remaining tolerant to moderate layout changes. Limitations and extensions are discussed, including support for additional fields, improved key similarity metrics, operational deployment with integrated feedback and broader benchmarking.<\/jats:p>","DOI":"10.3390\/a19020105","type":"journal-article","created":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T12:49:07Z","timestamp":1769690947000},"page":"105","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Template-Based Approach for Industrial Title Block Compliance Check"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-4326-4500","authenticated-orcid":false,"given":"Olivier","family":"Laurendin","sequence":"first","affiliation":[{"name":"Digital Excellence Center, Assystem, 92400 Courbevoie, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Khwansiri","family":"Ninpan","sequence":"additional","affiliation":[{"name":"Digital Excellence Center, Assystem, 92400 Courbevoie, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Quentin","family":"Robcis","sequence":"additional","affiliation":[{"name":"Digital Excellence Center, Assystem, 92400 Courbevoie, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Richard","family":"Lehaut","sequence":"additional","affiliation":[{"name":"Digital Excellence Center, Assystem, 92400 Courbevoie, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"H\u00e9l\u00e8ne","family":"Danlos","sequence":"additional","affiliation":[{"name":"Digital Excellence Center, Assystem, 92400 Courbevoie, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicolas","family":"Bureau","sequence":"additional","affiliation":[{"name":"Digital Excellence Center, Assystem, 92400 Courbevoie, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2378-8331","authenticated-orcid":false,"given":"Robert","family":"Plana","sequence":"additional","affiliation":[{"name":"Technology & Innovation, Assystem, 92400 Courbevoie, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,29]]},"reference":[{"key":"ref_1","unstructured":"(2025, December 23). Drawing SheetsTitle Blocks. Available online: https:\/\/www.roymech.co.uk\/Useful_Tables\/Drawing\/Title_blocks.html."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Toro, J.V., and Tarkian, M. (2025). Optimizing Text Recognition in Mechanical Drawings: A Comprehensive Approach. Machines, 13.","DOI":"10.3390\/machines13030254"},{"key":"ref_3","unstructured":"Najman, L., Gibot, O., and Barbey, M. (2001, January 7\u20138). Automatic Title Block Location in Technical Drawings. Proceedings of the 4th IAPR International Workshop on Graphics Recognition, Kingston, ON, Canada."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2327005","DOI":"10.1080\/08839514.2024.2327005","article-title":"Intelligent Extraction of Multi-style and Multi-template Title Block Information Based on Fuzzy Matching","volume":"38","author":"Li","year":"2024","journal-title":"Appl. Artif. Intell."},{"key":"ref_5","unstructured":"Lombardi, A., Duan, L., Elnagar, A., Zaalouk, A., Ismail, K., and Vakaj, E. (2025). Title Block Detection and Information Extraction for Enhanced Building Drawings Search. arXiv."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.procir.2024.10.041","article-title":"Automatic Raster Engineering Drawing Digitisation for Legacy Parts towards Advanced Manufacturing","volume":"129","author":"Maupou","year":"2024","journal-title":"Procedia CIRP"},{"key":"ref_7","first-page":"43","article-title":"A Study on Information Extraction Method of Engineering Drawing Tables","volume":"50","author":"Sulaiman","year":"2012","journal-title":"Int. J. Comput. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Kim, K.Y., Monplaisir, L., and Rickli, J. (2023). AI-Based Engineering and Production Drawing Information Extraction. Flexible Automation and Intelligent Manufacturing: The Human-Data-Technology Nexus, Springer.","DOI":"10.1007\/978-3-031-18326-3"},{"key":"ref_9","unstructured":"(2025, December 23). AI Feature Extraction for Technical Drawings. Available online: https:\/\/werk24.io\/."},{"key":"ref_10","unstructured":"(2026, January 22). Support | Kahua. Available online: https:\/\/resources.kahua.com\/customer-training-videos-specialty-apps-and-others\/part-4-using-title-block-extraction."},{"key":"ref_11","unstructured":"(2025, December 23). Automated Drawing Extraction | Autodesk. Available online: https:\/\/help.autodesk.com\/view\/DOCS\/ENU\/?guid=Automated_Drawing_Extraction."},{"key":"ref_12","unstructured":"(2025, December 23). laujan. Custom Template Document Model\u2014Document Intelligence\u2014Azure AI Services. Available online: https:\/\/learn.microsoft.com\/en-us\/azure\/ai-services\/document-intelligence\/train\/custom-template?view=doc-intel-4.0.0."},{"key":"ref_13","unstructured":"(2025, December 23). Custom Extractor Overview|Document AI. Available online: https:\/\/docs.cloud.google.com\/document-ai\/docs\/custom-extractor-overview."},{"key":"ref_14","unstructured":"(2025, December 23). Bedrock Data Automation Projects\u2014Amazon Bedrock. Available online: https:\/\/docs.aws.amazon.com\/bedrock\/latest\/userguide\/bda-projects.html."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Shen, Z., Zhang, R., Dell, M., Lee, B.C.G., Carlson, J., and Li, W. (2021). LayoutParser: A Unified Toolkit for Deep Learning Based Document Image Analysis. arXiv.","DOI":"10.1007\/978-3-030-86549-8_9"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Huang, Y., Lv, T., Cui, L., Lu, Y., and Wei, F. (2022). LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking. arXiv.","DOI":"10.1145\/3503161.3548112"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Kim, G., Hong, T., Yim, M., Nam, J., Park, J., Yim, J., Hwang, W., Yun, S., Han, D., and Park, S. (2022). OCR-free Document Understanding Transformer. arXiv.","DOI":"10.1007\/978-3-031-19815-1_29"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Smith, R. (2007, January 23\u201326). An Overview of the Tesseract OCR Engine. Proceedings of the Ninth International Conference on Document Analysis and Recognition (ICDAR 2007), Curitiba, Brazil.","DOI":"10.1109\/ICDAR.2007.4376991"},{"key":"ref_19","unstructured":"Cui, C., Sun, T., Lin, M., Gao, T., Zhang, Y., Liu, J., Wang, X., Zhang, Z., Zhou, C., and Liu, H. (2025). PaddleOCR 3.0 Technical Report. arXiv."},{"key":"ref_20","unstructured":"Mindee\/Doctr. Available online: https:\/\/github.com\/mindee\/doctr."},{"key":"ref_21","unstructured":"JaidedAI\/EasyOCR. Available online: https:\/\/github.com\/JaidedAI\/EasyOCR."},{"key":"ref_22","unstructured":"Facebookresearch\/Detectron2. Available online: https:\/\/github.com\/facebookresearch\/detectron2."},{"key":"ref_23","unstructured":"(2025, December 23). ISO 7200:2004. Available online: https:\/\/www.iso.org\/fr\/standard\/35446.html."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016). You Only Look Once: Unified, Real-Time Object Detection. arXiv.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_25","unstructured":"OpenAI, Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F.L., Almeida, D., Altenschmidt, J., and Altman, S. (2024). GPT-4 Technical Report. arXiv."},{"key":"ref_26","unstructured":"Wang, P., Bai, S., Tan, S., Wang, S., Fan, Z., Bai, J., Chen, K., Liu, X., Wang, J., and Ge, W. (2024). Qwen2-VL: Enhancing Vision-Language Model\u2019s Perception of the World at Any Resolution. arXiv."},{"key":"ref_27","unstructured":"laujan (2025, December 23). General Key-Value Extraction\u2014Document Intelligence\u2014Foundry Tools. Available online: https:\/\/learn.microsoft.com\/en-us\/azure\/ai-services\/document-intelligence\/prebuilt\/general-document?view=doc-intel-4.0.0."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Smock, B., Pesala, R., and Abraham, R. (2022, January 18\u201324). PubTables-1M: Towards Comprehensive Table Extraction from Unstructured Documents. Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00459"},{"key":"ref_29","first-page":"7","article-title":"COCO Annotator: Web-Based Image Segmentation Tool for Object Detection, Localization, and Keypoints","volume":"13","author":"Stefanics","year":"2022","journal-title":"ACM SIGMultimedia Rec."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/19\/2\/105\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T12:51:11Z","timestamp":1769691071000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/19\/2\/105"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,29]]},"references-count":29,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["a19020105"],"URL":"https:\/\/doi.org\/10.3390\/a19020105","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,29]]}}}