• Atik Sama
• Jaimin Dave
• Rushabh Malvaniya
• Rahul Rathod
• Nidhi Chhangani

With the development of cutting-edge technologies like machine learning and natural language processing (NLP), instruction analysis has changed. These methods concentrate on comprehending and analyzing both structured and unstructured educational data. These days, computerized systems are taking the role of manual review methods. This change enhances the speed and precision of deciphering intricate instructions. Prevailing techniques include text summarization, object detection, and semantic segmentation. They help in extracting essential information, identifying components, and mapping workflows. Computer vision also plays a key role in analyzing visual instructions and diagrams. Together, these methods create a detailed understanding of instructional content. In our project, these techniques are applied to automate product assembly manual analysis. AI models process both text and images to generate structured, easy-to-follow assembly steps. This approach reduces manual effort, minimizes errors, and enhances user experience. It lays the groundwork for smarter, AI-assisted instruction systems.

Automated Assembly Instructions, Computer Vision, Graph Neural Networks, Natural Language Processing, Large Language Models, 3D Scene Understanding, Task Sequencing, Instruction Generation, Smart Manufacturing, AI in Documentation.

"AUTOMATED ANALYSIS OF PRODUCT ASSEMBLY INSTRUCTION USING AI", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 4, page no.l635-l642, April-2025, Available :http://www.jetir.org/papers/JETIR2504B78.pdf

"AUTOMATED ANALYSIS OF PRODUCT ASSEMBLY INSTRUCTION USING AI", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 4, page no. ppl635-l642, April-2025, Available at : http://www.jetir.org/papers/JETIR2504B78.pdf