Automated Indexing of Historical Postcards: An End-to-End Approach Combining Image and Text Analysis

Historical postcards are of great interest to different researchers and especially historians. Preserving these documents for future generations is important. Indeed, they represent valuable cultural heritage documents combining visual imagery with textual information, but manual indexing of large digitized collections is prohibitively expensive in time and resources. This paper presents an end-to-end approach for automated historical postcard indexing that integrates computer vision and natural language processing techniques. Our modular pipeline employs fine-tuned YOLO models to detect text types and postal markings, EasyOCR for text transcription, CamemBERT for named entity recognition enabling automatic geolocation, and DINOv2 models for visual content classification across ten semantic topics. Support Vector Machines predict keywords for images lacking textual descriptions using DINOv2 image embeddings, while complex postal markings are segmented and transcribed. We demonstrate our approach on the recto (illustrated front side) of 4,293 digitized postcards from the Grand Est region of France (1899-1930). Our results show that, among the latest YOLO models, fine-tuned YOLOv12x achieves 0.83 weighted average precision for detection. Fine-tuned DINOv2 attains 0.91 weighted average precision for image classification, while CLIP and other classification models do not exceed 0.83. Finally, named entity recognition successfully geolocates 52% of postcards. In fully automatic operation, the complete pipeline assigns at least a coarse topic to almost every postcard and at least one richer field (a keyword, a geolocation, or a date) to about 80% of them, leaving fewer than 1% essentially un-indexed. Our work contributes to digital humanities by demonstrating effective integration of multiple AI techniques for automated heritage document analysis and provides a publicly available dataset for future research.