Deep Learning and Cultural Heritage: The CEPROQHA Project Case Study Cultural heritage takes an important part of the history of humankind as it is one of the most powerful tools for the transfer and preservation of moral identity. As a result, these cultural assets are considered highly valuable and sometimes priceless. Digital technologies provided multiple tools that address challenges related to the promotion and information access in the cultural context. However, the large data collections of cultural information have more potential to add value and address current challenges in this context with the recent progress in artificial intelligence (AI) with deep learning and data mining tools. Through the present paper, we investigate several approaches that are used or can potentially be used to promote, curate, preserve and value cultural heritage through new and evolutionary techniques based on deep learning tools. The deep learning approaches entirely developed by our team are intended to classify and annotate cultural data, complete missing data, or map existing data schemes and information to standardized schemes with language processing tools.

A Comprehensive Study of ImageNet PreTraining for Historical Document Image Analysis Automatic analysis of scanned historical documents comprises a wide range of image analysis tasks, which are often challenging for machine learning due to a lack of humanannotated learning samples. With the advent of deep neural networks, a promising way to cope with the lack of training data is to pretrain models on images from a different domain and then finetune them on historical documents. In the current research, a typical example of such crossdomain transfer learning is the use of neural networks that have been pretrained on the ImageNet database for object recognition. It remains a mostly open question whether or not this pretraining helps to analyse historical documents, which have fundamentally different image properties when compared with ImageNet. In this paper, we present a comprehensive empirical survey on the effect of ImageNet pretraining for diverse historical document analysis tasks, including character recognition, style classification, manuscript dating, semantic segmentation, and contentbased retrieval. While we obtain mixed results for semantic segmentation at pixellevel, we observe a clear trend across different network architectures that ImageNet pretraining has a positive effect on classification as well as contentbased retrieval.