In this paper, we present a new dynamic classifier design based on a set of one-class independent SVM for image data stream categorization. Dynamic or continuous learning and classification has been recently investigated to deal with different situations, like online learning of fixed concepts, learning in non-stationary environments (concept drift) or learning from imbalanced data. Most of solutions are not able to deal at the same time with many of these specificities. Particularly, adding new concepts, merging or splitting concepts are most of the time considered as less important and are consequently less studied, whereas they present a high interest for stream-based document image classification. To deal with that kind of data, we explore a learning and classification scheme based on one-class SVM classifiers that we call mOC-iSVM (multi-one-class incremental SVM). Even if one-class classifiers are suffering from a lack of discriminative power, they have, as a counterpart, a lot of interesting properties coming from their independent modeling. The experiments presented in the paper show the theoretical feasibility on different benchmarks considering addition of new classes. Experiments also demonstrate that the mOC-iSVM model can be efficiently used for tasks dedicated to documents classification (by image quality and image content) in a context of streams, handling many typical scenarii for concepts extension, drift, split and merge.