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Abstract To use the benefits of Advanced Driver Assistance Systems (ADAS)-Tests in simulation and reality a new approach for using Augmented Reality (AR) in an automotive vehicle for testing ADAS is presented in this paper. Our procedure provides a link between simulation and reality and should enable a faster development process for future increasingly complex ADAS tests and future mobility solutions. Test fields for ADAS offer a small number of orientation points. Furthermore, these must be detected and processed at high vehicle speeds. That requires high computational power both for developing our method and its subsequent use in testing. Using image segmentation (IS), artificial intelligence (AI) for object recognition, and visual simultaneous localization and mapping (vSLAM), we aim to create a three-dimensional model with accurate information about the test site. It is expected that using AI and IS will significantly improve performance as computational speed and accuracy for AR applications in automobiles.

Handwritten Text Recognition (HTR) is essential for digitizing historical documents in different kinds of archives. In this study, we introduce a hybrid form archive written in French: the Belfort civil registers of births. The digitization of these historical documents is challenging due to their unique characteristics such as writing style variations, overlapped characters and words, and marginal annotations. The objective of this survey paper is to summarize research on handwritten text documents and provide research directions toward effectively transcribing this French dataset. To achieve this goal, we presented a brief survey of several modern and historical HTR offline systems of different international languages, and the top state-of-the-art contributions reported of the French language specifically. The survey classifies the HTR systems based on techniques employed, datasets used, publication years, and the level of recognition. Furthermore, an analysis of the systems’ accuracies is presented, highlighting the best-performing approach. We have also showcased the performance of some HTR commercial systems. In addition, this paper presents a summarization of the HTR datasets that publicly available, especially those identified as benchmark datasets in the International Conference on Document Analysis and Recognition (ICDAR) and the International Conference on Frontiers in Handwriting Recognition (ICFHR) competitions. This paper, therefore, presents updated state-of-the-art research in HTR and highlights new directions in the research field.

Vital records are rich of meaningful historical data concerning city as well as countryside inhabitants that can be used, among others, to study former populations and then reveal the social, economic and demographic characteristics of those populations. However, these studies encounter a main difficulty for collecting the data needed since most of these records are scanned documents that need a manual transcription step in order to gather all the data and start exploiting it from a historical point of view. This step consequently slows down the historical research and is an obstacle to a better knowledge of the population habits depending on their social conditions. Therefore in this paper, we present a modular and self-sufficient analysis pipeline using state-of-the-art algorithms mostly regardless of the document layout that aims to automate this data extraction process.