Case Study:

AI-Enhanced Document Digitization and Retrieval System


In the digital age, public libraries face the monumental task of not only preserving their vast collections but also making them easily accessible to patrons. One well-established library, boasting an extensive archive of books, magazines, and various documents, recognized the need to digitize its entire collection. However, the library sought more than simple digitization; they wanted to enhance the usability of their digital archive by extracting metadata and generating summaries for each document.

To achieve this ambitious goal, the library partnered with SBL Technologies, a leading provider of AI-driven solutions. SBL Technologies was tasked with developing a sophisticated AI-based system that would seamlessly integrate with the library’s existing management software, revolutionizing the way patrons access and interact with the library’s digital resources.



Who we worked with:

A well-established public library with a vast collection of books, magazines, and various documents.


What the customer needed:

  • To digitize their entire archive, including books, magazines, and various documents.
  • To extract metadata and generate summaries for each digitized document to enhance accessibility and usability.
  • To integrate the AI-based solution seamlessly with the library’s existing management system.
  • To provide contextual responses to user queries based on the digitized content.


How we helped:

  • Developed an AI-powered OCR and text extraction system capable of processing a wide range of document types and conditions.
  • Implemented AI-driven metadata generation and an automated indexing system to organize extracted information.
  • Integrated a large language model to automatically generate concise document summaries and provide contextual responses to user queries.
  • Seamlessly integrated the AI solution with the library’s existing management system and designed a user-friendly interface for easy access to digitized content



The primary challenge faced by the public library was the immense volume and variety of documents that required digitization. Traditional methods, relying heavily on manual labour for OCR, indexing, and summary generation, would have been prohibitively time-consuming and expensive. Furthermore, the library needed a way to provide contextual responses to user queries, adding an additional layer of complexity to the digitization process.






The solution involved several key AI technologies and processes:

  • AI-Powered OCR and Text Extraction:
    • Advanced OCR Technology: Utilized state-of-the-art OCR tools capable of recognizing text across a wide range of document types and conditions, from pristine books to worn-out magazines.
    • Text Normalization: Applied natural language processing (NLP) techniques to clean and normalize the extracted text, ensuring high accuracy and usability.


  • Metadata Extraction and Indexing:
    • AI-Driven Metadata Generation: Employed machine learning algorithms to automatically identify and extract key information (metadata) such as author, publication date, keywords, and topics from each document.
    • Automated Indexing System: Developed a dynamic indexing system that organizes extracted metadata, making it searchable within the library’s database.


  • Summary Generation and User Query Handling:
    • Large Language Model Integration: Integrated a large language model, extensively trained on a diverse set of documents, to automatically generate concise summaries of each document.
    • Contextual Query Response System: The language model also powered a query response system that provides users with information relevant to their specific requests based on the document context.


  • System Integration and User Interface:
    • Library Management System Integration: Seamlessly integrated the AI solution with the existing library management system, allowing for smooth operations and minimal disruption.
    • User-Friendly Interface: Designed an intuitive user interface that enables easy access to digitized documents, metadata, and summaries.





The implementation of this AI-based solution provided the library with multiple significant benefits:


  • Efficiency and Speed: Reduced the time needed for document processing by over 80%, enabling quicker access to newly digitized content.


  • Accuracy and Reliability: Enhanced the accuracy of text recognition and metadata extraction, improving the overall quality of the digitized archive.


  • Improved Accessibility: Users can now easily search for and retrieve documents based on rich metadata and summaries, significantly enhancing user experience.


  • Scalability: The system is designed to accommodate future expansions of the library’s collection without requiring proportional increases in resources.



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