Deep Learning for Historical Data Analysis

Mathieu Aubry

Abstract: This presentation will give an overview of projects on leveraging deep learning for historical data analysis my group did in the last 3 years, partly in the context of the ANR EnHerit project. I will first discuss how deep learning can be used to retrieve and analyze repeated details in artworks in artwork collections [5, 6]. I will then present several problems related to historical document analysis: historical watermarks recognition [7], document images segmentation [2], clustering for text modelling [3, 4], and scientific illustration propagation in historical manuscripts analysis [1]. In all cases, I will show that standard approaches can give useful baseline results when tuned adequately, but that developing dedicated approaches that take into account the specificity of the data and the problem significantly improves the results.