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This study fine-tunes Florence-2, an open-source vision-language model, using 400 drawings with ground-truth annotations from domain experts. GPT-4o and Claude-3.5-Sonnet are compared on the same dataset, using zero-shot evaluation and metrics including precision, recall, F1-score, and hallucination rate. Florence-2 shows improved precision, recall, F1-score, and reduced hallucination, demonstrating efficient automation for downstream manufacturing tasks.",{"@graph":69,"@context":121},[70,84,104],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/fine-tuning-vision-language-model-for-automated-engineering-drawing-information-extraction/663912/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":98,"encodingFormat":97,"isAccessibleForFree":99,"interactionStatistic":100},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/fine-tuning-vision-language-model-for-automated-engineering-drawing-information-extraction/663912.png","ImageObject",300,407,{"name":92,"@type":93},"Jasmine","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29",true,{"@type":101,"interactionType":102,"userInteractionCount":14},"InteractionCounter",{"@type":103},"ViewAction",{"@type":105,"mainEntity":106},"FAQPage",[107,113,117],{"name":108,"@type":109,"acceptedAnswer":110},"What problem does the study address in GD&T extraction from engineering drawings?","Question",{"text":111,"@type":112},"The study targets the time-consuming and labor-intensive process of extracting GD&T information from 2D engineering drawings, which often relies on manual or semi-automated approaches.","Answer",{"name":114,"@type":109,"acceptedAnswer":115},"How is Florence-2 used in the proposed approach?",{"text":116,"@type":112},"Florence-2 is fine-tuned using full-parameter fine-tuning on a dataset of 400 drawings with ground-truth annotations, across three experiments with differently augmented datasets.",{"name":118,"@type":109,"acceptedAnswer":119},"Which models are compared and what evaluation metrics are used?",{"text":120,"@type":112},"The study compares Florence-2 with closed-source VLMs GPT-4o and Claude-3.5-Sonnet in zero-shot settings, evaluating precision, recall, F1-score, and hallucination rate.","https://schema.org",{"og:url":83,"og:type":123,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":125,"canonical":83},"index,follow",{"doc_id":127,"site_id":62},663912,1793234078,{"code":4,"msg":5,"data":130},{"doc_id":127,"user_id":131,"nickname":92,"user_avatar":132,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":133,"file_id":134,"file_url":135,"file_type":136,"file_size":137,"view_count":14,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":39,"language":138,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":139,"faqs":140,"seo_title":141,"seo_description":67,"update_tm":128,"read_time":46},2336478487870,"https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45","Fine-Tuning Vision-Language Model for Automated Engineering Drawing  \nInformation Extraction  \nMuhammad Tayyab Khan 1, 3, Lequn Chen2, Ye Han Ng2, Wenhe Feng 1, Nicholas Yew Jin Tan 1, Seung Ki Moon3  \n1 Singapore Institute of Manufacturing Technology (SIMTech), Agency for Science, Technology and Research (A*STAR), 5 CleanTech Loop, \\#01-01 CleanTech Two Block B, Singapore 636732, Republic of Singapore  \n2 Advanced Remanufacturing and Technology Centre (ARTC), Agency for Science, Technology and Research (A*STAR), 3 CleanTech Loop, \\#01-01 CleanTech Two, Singapore 637143, Republic of Singapore  \n3 School of Mechanical and Aerospace Engineering, Nanyang Technological University, 639798, Singapore  \nAbstract  \nGeometric Dimensioning and Tolerancing (GD&T) plays a critical role in manufacturing by defining acceptable variations in part features to ensure component quality and functionality. However, extracting GD&T information from 2D engineering drawings is a time-consuming and labor-intensive task, often relying on manual efforts or semi-automated tools. To address these challenges, this study proposes an automated and computationally efficient GD&T extraction method by fine-tuning Florence-2, an open-source vision-language model (VLM) . The model is trained on a dataset of 400 drawings with ground truth annotations provided by domain experts. For comparison, two state-of-the-art closed-source VLMs, GPT-4o and Claude- 3.5-Sonnet, are evaluated on the same dataset. All models are assessed using precision, recall, F1-score, and hallucination metrics. Due to the computational cost and impracticality of fine-tuning large closed-source VLMs for domain-specific tasks, GPT-4o and Claude-3.5-Sonnet are evaluated in a zero-shot setting. In contrast, Florence-2, a smaller model with 0.23 billion parameters, is optimized through full-parameter fine-tuning across three distinct experiments, each utilizing datasets augmented to different levels. The results show that Florence-2 achieves a 29.95% increase in precision, a 37.75% increase in recall, a 52.40% improvement in F1-score, and a 43.15% reduction in hallucination rate compared to the best-performing closed-source model. These findings highlight the effectiveness of fine-tuning smaller, open-source VLMs like Florence-2, offering a practical and efficient solution for automated GD&T extraction to support downstream manufacturing tasks.  \nKeywords: Geometric Dimensioning and Tolerancing (GD&T), 2D Engineering Drawings, Vision-Language Models, FineTuning, Advanced Manufacturing, Automated Information Extraction.  \n1 Introduction  \nGeometric Dimensioning and Tolerancing (GD&T) is essential in manufacturing for specifying allowable variations in part features, ensuring that components meet precise functional and quality standards [1] . GD&T uses standardized symbols and rules to clearly communicate geometric requirements on engineering drawings, with Feature Control Frames (FCFs) specifying tolerances, geometric characteristics, and relevant datums. Accurate interpretation of GD&T symbols is crucial for tasks such as inspection, quality control, and assembly, as even minor misinterpretations can lead to production defects and non-conforming parts [2] .  \nTraditionally, extracting GD&T information from 2D engineering drawings has been a manual process , often involving techniques like ballooning, where engineers manually mark and label features [3] . While tools such as Mitutoyo’sMeasurLink [4] assist in these processes, these manual methods remain slow, error-prone, and difficult to scale in high-volume production environments. Additionally, variability in interpretation and data entry errors can result in costly rework and production delays [5] .  \nRecent advancements in machine learning methods, such as object detection algorithms (YOLO [6]) and Optical Character Recognition (OCR) technologies (Tesseract [7]), have been applied to automate GD&T extraction, reducing manual effort. However, ","cbCaivGgvDiICtOD","https://ap.wps.com/l/cbCaivGgvDiICtOD","pdf",759905,"English","# Abstract\n# Keywords\n# 1 Introduction\n# 2 Methodology Overview","[{\"question\":\"What problem does the study address in GD\\u0026T extraction from engineering drawings?\",\"answer\":\"The study targets the time-consuming and labor-intensive process of extracting GD\\u0026T information from 2D engineering drawings, which often relies on manual or semi-automated approaches.\"},{\"question\":\"How is Florence-2 used in the proposed approach?\",\"answer\":\"Florence-2 is fine-tuned using full-parameter fine-tuning on a dataset of 400 drawings with ground-truth annotations, across three experiments with differently augmented datasets.\"},{\"question\":\"Which models are compared and what evaluation metrics are used?\",\"answer\":\"The study compares Florence-2 with closed-source VLMs GPT-4o and Claude-3.5-Sonnet in zero-shot settings, evaluating precision, recall, F1-score, and hallucination rate.\"}]","Fine-Tuning Vision-Language Model for Automated Engineering Drawing - Information Extraction | PDF"]