For the 9000 train accidents reported each year in the European Union , the Recording Strip (RS) and Filling-Card
(FC) related to the train activities represent the only usable evidence for SNCF (the French railway operator) and most of
National authorities. More precisely, the RS contains information about the train journey, speed and related Driving
Events (DE) such as emergency brakes, while the FC gives details on the departure/arrival stations. In this context, a
complete checking for 100% of the RS was recently voted by French law enforcement authorities (instead of the 5%
currently performed), which raised the question of an automated and efficient inspection of this huge amount of
recordings. To do so, we propose a machine vision prototype, constituted with cassettes receiving RS and FC to be
digitized. Then, a video analysis module firstly determines the type of RS among eight possible types; time/speed curves
are secondly extracted to estimate the covered distance, speed and stops, while associated DE are finally detected using
convolution process. A detailed evaluation on 15 RS (8000 kilometers and 7000 DE) shows very good results (100% of
good detections for the type of band, only 0.28% of non detections for the DE). An exhaustive evaluation on a panel of
about 100 RS constitutes the perspectives of the work.
This paper describes a new automatic color thresholding based on wavelet denoising and color clustering with K-means in order to segment text information in a camera-based image. A particular focus is given on stroke analysis to improve character segmentation, the step which follows color thresholding. Several parameters bring different information and this paper tries to explain how to use this complementarity. It is mainly based on the discrimination between two kinds of backgrounds: clean or complex. On one hand, this separation is useful to apply a particular algorithm on each of these cases and on the other hand to decrease the computation time for clean cases for which a faster method could be considered. Finally, several experiments were done to discuss results and to conclude that the use of a discrimination between kinds of backgrounds gives better results in terms of Precision and Recall. This separation of backgrounds is done with supervised classification. After tests with several classifiers (linear and quadratic discriminant analysis, K-nearest neighbors, neural networks and support vector machines), best results are given with a set of features based on properties of the gray-level histogram and by using a support vector machine.