A Comprehensive Study of ImageNet PreTraining for Historical Document Image Analysis Automatic analysis of scanned historical documents comprises a wide range of image analysis tasks, which are often challenging for machine learning due to a lack of humanannotated learning samples.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cope with the lack of training data is to pretrain models on images from a different domain and then finetune them on historical documents.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 It remains a mostly open question whether or not this pretraining helps to analyse historical documents, which have fundamentally different image properties when compared with ImageNet. In this paper, we present a comprehensive empirical survey on the effect of ImageNet pretraining for diverse historical document analysis tasks, including character recognition, style classification, manuscript dating, semantic segmentation, and contentbased retrieval.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.

Text Line Segmentation in Historical Document Images Using an Adaptive UNet Architecture On most document image transcription, indexing and retrieval systems, text line segmentation remains one of the most important preliminary task. Hence, the research community working in document image analysis is particularly interested in providing reliable text line segmentation methods. Recently, an increasing interest in using deep learningbased methods has been noted for solving various subfields and tasks related to the issues surrounding document image analysis. Thanks to the computer hardware and software evolution, several methods based on using deep architectures continue to outperform the pattern recognition issues and particularly those related to historical document image analysis. Thus, in this paper we present a novel deep learningbased method for text line segmentation of historical documents. The proposed method is based on using an adaptive UNet architecture. Qualitative and numerical experiments are given using a large number of historical document images collected from the Tunisian national archives and different recent benchmarking datasets provided in the context of ICDAR and ICFHR competitions. Moreover, the results achieved are compared with those obtained using the stateoftheart methods.