[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119429-en":3,"doc-seo-119429-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119429,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Towards an Explainable Machine Learning Framework for Sketched Diagram Recognition","Recent progress in machine learning has improved image recognition, yet many models remain difficult to interpret, limiting users’ ability to trust and understand predictions. This challenge is especially significant for sketch and diagram recognition, where clarity about decision-making is critical. The research develops an explainable machine learning framework that uses feature visualization and feature attribution (including SHAP) to reveal influential patterns. The approach targets both higher recognition performance and stronger interpretability to support usable, trustworthy sketch recognition.","Towards an Explainable Machine Learning Framework for Sketched Diagram Recognition⋆  \nAmardeep Singh 1, * , Md Athar Imtiaz2 and Rachel Blagojevic3  \n1 UCOL-Te Pkenga, Palmerston North, New Zealand  \n2 Massey University, Palmerston North, New Zealand  \n3 Massey University, Palmerston North, New Zealand  \nAbstract  \nIn recent years, machine learning has made significant advancements in various fields, including image recognition. However, the complexity of these models often makes it difficult for users to understand the reasoning behind their predictions. This is especially true for sketch recognition, where the ability to understand and explain the model’s decision-making process is crucial. To address this issue, our research focuses on developing an explainable machine learning framework for sketch recognition. The framework incorporates techniques such as feature visualization and feature attribution methods which provide insights into the model’s decision-making process. The goal of this research is to not only improve the performance of sketch recognition models but also to increase their interpretability, making them more usable and trustworthy for users.  \nKeywords  \nExplainable AI, SHAP, Sketch recognition, Digital ink recognition, Diagram recognition  \n1. Introduction  \nThe task of creating diagrams on a computer using a traditional mouse and keyboard can be a difficult task compared to the ease of drawing with a pen and paper. To bridge this gap, stylus-based devices are used to provide a similar user experience to paper-based sketching. Recognizing these sketches, or identifying elements in the drawing, can enhance the user experience by allowing for advanced functionalities such as automatic beautification, intelligent editing, and animation of the content. However, a challenge in the field of sketch recognition is maintaining high accuracy while still allowing for a free-sketch environment similar to traditional pen and paper. Even though recognition techniques have become more sophisticated, it is difficult to understand the inner workings of blackbox machine learning methods [1, 2, 3] . Without a deeper understanding, it is hard to make substantial improvements to the recognition algorithm’s accuracy. In this research, we applied explainable AI techniques to assist in understanding how a machine learning based sketch recognition algorithm classifies instances. We believe this use of explainable AI (XAI) will lead to improved accuracy in future sketch recognition  \nJoint Proceedings of the ACMIUI Workshops 2023, March 2023, Sydney, Australia  \n⋆A. Singh, M. A. Imtiaz, and R. Blagojevic. 2023. Towards an Explainable Machine Learning Framework. In Joint Proceedings of the ACM IUI 2023 Workshops. Sydney, Australia, 7 pages  \n* Corresponding author.  \n$ [a.singh@ucol.ac.nz](a.singh@ucol.ac.nz) (A. Singh); [a.imtiaz@massey.ac.nz](a.imtiaz@massey.ac.nz)[ ](a.imtiaz@massey.ac.nz)(M. A. Imtiaz); [r.v.blagojevic@massey.ac.nz](r.v.blagojevic@massey.ac.nz) (R. Blagojevic)  \n􀀚 0000-0003-1916-3347 (A. Singh)  \n© 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License  \n\n|  | CEUR Workshop Proceedings |\n| --- | --- |\n\nAttribution 4 .0 International (CC BY 4 .0) .  \nCEUR Workshop Proceedings ([CEUR-WS.org](CEUR-WS.org))  \n[http://ceur-ws.org](http://ceur-ws.org)  \n[ISSN 1613-0073](ISSN 1613-0073)  \ntechniques. The main contributions of this work can be summarized as follow:  \n• Inside the blackbox: We are able to provide insights into the inner workings of a blackbox machine learning model for sketch recognition. By using techniques such as feature visualization and feature attribution methods like SHAP, weare able to provide a clear understanding of the model’s decision-making process. This is important because it allows researchers to understand how the model is able to classify sketches and identify the important features that contribute to the predictions.  \n• Methodology for understandin","cbCaibRYI6hBeAJ0","https://ap.wps.com/l/cbCaibRYI6hBeAJ0","pdf",3191384,1,7,"English","en",105,"# Introduction\n## Explainable AI for sketch recognition\n# Related work\n## Global vs local interpretability\n## XAI techniques (Attention, LIME, Saliency, Counterfactuals, Distillation, SHAP)","[{\"question\":\"Why is explainability important in sketched diagram recognition?\",\"answer\":\"Users need to understand why a model makes a particular classification to trust predictions. Explainability is especially important for free-sketch settings where accuracy and interpretability must both be addressed.\"},{\"question\":\"What techniques does the proposed framework use to explain model decisions?\",\"answer\":\"The framework incorporates feature visualization and feature attribution methods, including SHAP, to provide insights into the model’s decision-making process.\"},{\"question\":\"How does the paper position its contribution to the sketch recognition field?\",\"answer\":\"It provides insights into black-box model behavior and outlines a methodology to help future work understand and improve black-box sketch recognizers, supporting trust and further development.\"}]","Towards an Explainable Machine Learning Framework for Sketched Diagram Recognition | PDF",1785724247,18,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"towards-an-explainable-machine-learning-framework-for-sketched-diagram-recognition","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/towards-an-explainable-machine-learning-framework-for-sketched-diagram-recognition/119429/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is explainability important in sketched diagram recognition?","Question",{"text":75,"@type":76},"Users need to understand why a model makes a particular classification to trust predictions. Explainability is especially important for free-sketch settings where accuracy and interpretability must both be addressed.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What techniques does the proposed framework use to explain model decisions?",{"text":80,"@type":76},"The framework incorporates feature visualization and feature attribution methods, including SHAP, to provide insights into the model’s decision-making process.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the paper position its contribution to the sketch recognition field?",{"text":84,"@type":76},"It provides insights into black-box model behavior and outlines a methodology to help future work understand and improve black-box sketch recognizers, supporting trust and further development.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]