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AI-Driven Media Search Engine for Archival Film Archives

websensa.com

Media

Information technology

Challenges in Media Searchability and Metadata Enrichment

Existing manually-entered metadata limited search effectiveness for archival Polish films, making it difficult to locate specific scenes containing objects, people, locations, or audio elements. Required automated solution to enhance search accuracy and accessibility while handling monochrome and low-quality historical footage.

About the Client

Poland's largest film production center with 70-year heritage, requiring advanced search capabilities for archival audiovisual materials

Project Goals for Enhanced Media Search

Develop AI-driven search engine for audiovisual materials
Automatically tag film elements (objects, people, locations, emotions, sounds)
Improve search accuracy for archival black-and-white films
Enhance user experience through enriched metadata integration

Core System Functionalities

Object recognition in video content (architectural elements, objects, people)
Audio analysis for sound identification and speech detection
Emotion recognition in visual content
Cross-modal search combining text, audio, and visual elements
Integration with existing metadata management systems

Technology Stack Requirements

AI/ML frameworks (TensorFlow/PyTorch)

Computer vision models

Audio processing pipelines

RESTful API architecture

Cloud-based processing infrastructure

System Integration Needs

Existing media portal integration
Legacy database connectivity
User authentication systems
Content delivery network (CDN)

Performance and Security Requirements

High accuracy in object detection (95%+)
Scalable processing for 100,000+ hours of content
Low-latency search response times
Data security compliance (GDPR)
Compatibility with monochrome/low-quality footage

Expected Business Impact of Enhanced Media Search

Projected 300% improvement in search efficiency, enabling users to locate specific scenes within seconds. Expected to increase portal usage by 200% through improved discoverability of archival content. Will reduce manual metadata entry costs by 60% while maintaining 99.9% system availability.