The rapid digitization across various industries has led to an exponential increase in document complexity. While traditional document search systems have handled textual formats with relative ease, they often fall short in accurately retrieving information from documents containing hard tables and embedded images. This has been a persistent pain point for sectors like finance, HR policy, medicine, and engineering, where vital information is often trapped in these complex structures. Recognizing this challenge, Amity Solutions has developed an enhanced version of its Document Search Optimizer, designed specifically to address the nuanced demands of modern document retrieval.
Amity Document Search Optimizer V.2 takes our service search engine to the next level by combining advanced retrieval with Vision language model inspired from this paper [1] and our expertise in knowledge management. This upgraded version enhances search accuracy, improves information retrieval, and streamlines access to critical data. Designed to handle complex document structures— including HTML, DOCX, PDF, and visually enriched PDF slides—Amity Document Search Optimizer V.2 ensures that important insights are not lost. With smarter search capabilities and seamless integration, it delivers faster, more relevant results, empowering users to find the right information with ease and efficiency.
The diagrammatic representation of our Document Search Optimizer showcases its ability to efficiently extract both visual and textual content. The system is designed to determine the necessity for visual content extraction before processing. By employing a chunking method, the extracted content is broken down into manageable parts, optimizing search precision and speed.
The development team rigorously tested Amity Document Search Optimizer V.2 with approximately 300 test cases spanning industries like finance, HR policy, engineering and healthcare, using varied file formats. The results were compared across several search methodologies, including Vector Search, Azure Cognitive Search and Google Vertex AI Search. The new version of Amity's optimizer demonstrated impressive improvements: finance documents saw optimized search success rates increase to 87%, while HR and medical documents achieved rates of 91% and 80%, respectively. This robustness in retrieval showcases the optimizer's capability to handle diverse and complex document structures effectively.
An additional highlight of the new version is its enhanced vision capabilities, which have been particularly impactful in the engineering document. By integrating advanced vision techniques, the optimizer efficiently extracts and interprets data from diagrams and technical illustrations often found in engineering documents, improved to 93.5% search accuracy.
The optimizer significantly enhances document retrieval accuracy, especially for documents with complex elements like complex tables and embedded images, leading to faster and more reliable access to critical data.
The optimizer supports a wide range of file types, ensuring consistent processing and retrieval performance across HTML, DOCX, PDFs, and more, enhancing usability across different document formats.
By reducing the manual effort required to extract information from complex documents, organizations can focus on data-driven insights and decision-making, boosting overall productivity and efficiency.
In summary, Amity Document Search Optimizer V.2 represents a formidable leap forward in document retrieval technology. By expanding its capabilities to manage complex documents effectively, it addresses a critical gap in current systems, empowering organizations with enhanced data accessibility and operational efficiency. As industries continue to evolve, tools like these will be indispensable in navigating the complexities of modern information management.
[1] Faysse, Manuel, Hugues Sibille, Tony Wu, Bilel Omrani, Gautier Viaud, Céline Hudelot, and Pierre Colombo. "ColPali: Efficient Document Retrieval with Vision Language Models." arXiv, July 2, 2024. https://doi.org/10.48550/arXiv.2407.01449.