For media organizations, rapid access to historical content and production-related documentation is essential to support daily operations, research, and content creation. As archives continue to grow, traditional file storage methods and manual retrieval processes can become significant obstacles to efficiency.

VRT, the public broadcaster serving the Flemish Community of Belgium, faced these challenges with an extensive archive containing program scripts, production reports, technical documentation, and historical records accumulated over decades. To improve accessibility and prepare its archive for future growth, VRT launched a digital transformation initiative based on IRISNext. By introducing intelligent indexing, OCR technology, automated metadata extraction, and full-text search capabilities, the broadcaster transformed a complex archive into a structured and searchable information environment.

VRT (Vlaamse Radio- en Televisieomroeporganisatie) is the public broadcaster for the Flemish Community of Belgium. Operating from Brussels, VRT manages major television channels including VRT 1, VRT Canvas, and Ketnet, multiple radio stations, and the digital streaming platform VRT MAX. Supported primarily by funding from the Flemish government, VRT plays an important role in journalism, culture, and public information throughout Belgium.

Prior to the implementation of IRISNext, VRT maintained a large collection of both digital and physical documents within an unstructured network directory. Accessing information often required manual browsing through folders and files, making archival research particularly challenging for older productions. The objective of the project was to digitize, organize, and make these archives accessible through an intuitive web-based platform. The archive contained a wide variety of documents, including production reports, program scripts, shot lists, and technical records spanning multiple decades. The initiative focused on improving accessibility, increasing search efficiency, and ensuring the long-term usability of valuable archival content.

VRT’s archive consists of more than 10 terabytes of documentation stored across shared network locations. Accessing information efficiently was difficult due to inconsistent file structures, varying document formats, and the absence of structured metadata.

Employees regularly encountered challenges when searching for relevant information. In many cases, documents had to be opened manually and reviewed individually before their contents could be identified. The diversity of document formats also created difficulties in displaying and previewing files consistently. Because metadata was not available, searches depended entirely on folder names and filenames, limiting the effectiveness of retrieval. The lack of standardized categorization further complicated archival research, especially when locating historical information.

The existing environment relied solely on a traditional file directory structure that offered no metadata indexing or advanced search functionality. Without OCR processing or automated classification capabilities, locating information remained a slow and labor-intensive process. While integration with external systems was not required, the new solution needed to support large-scale archives and provide a scalable foundation for future growth.

To address these challenges, VRT partnered with IRIS IMS to implement a document management platform capable of processing a wide variety of document formats while delivering intuitive search functionality and structured information management.

The project followed a phased implementation approach. During the analysis and planning phase, project objectives were defined and methods for metadata extraction were established. This was followed by the creation of import and metadata extraction processes that enabled documents to be scanned, analyzed, categorized, and stored in a structured manner. OCR technology and full-text search capabilities were then introduced, allowing users to search documents based on their actual content rather than filenames alone.

Subsequent testing and optimization activities refined both search functionality and import procedures to accommodate differences in document structures and archive formats. The rollout was then completed progressively, supported by user training to ensure employees could quickly adopt the new system and take full advantage of its capabilities.

IRISNext served as the central platform throughout the project, providing OCR functionality, content services, and intelligent document processing to convert archive content into searchable PDF formats. Automated metadata extraction helped organize and classify documents consistently, while advanced search tools enabled employees to retrieve information within seconds. Flexible file rendering capabilities ensured reliable access to documents originating from different eras and stored in a variety of formats.