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In this paper, we apply an Information Retrieval model for the writer identification task. A set of local features is defined by clustering the graphemes produced by a segmentation procedure. Then a textual based Information Retrieval model is applied. After a first indexation step, this model no longuer requires image access to the database for responding(More)
In this paper, we show that both the writer identification and the writer verification tasks can be carried out using local features such as graphemes extracted from the segmentation of cursive handwriting. We thus enlarge the scope of the possible use of these two tasks which have been, up to now, mainly evaluated on script handwritings. A textual based(More)
In this paper, we present a multi-stream approach for off-line handwritten word recognition. The proposed approach combines low level feature streams namely, density based features extracted from 2 different sliding windows with different widths, and contour based features extracted from upper and lower contours. The multi-stream paradigm provides an(More)
In this paper, a new information extraction system by statistical shallow parsing in unconstrained handwritten documents is introduced. Unlike classical approaches found in the literature as keyword spotting or full document recognition, our approch relies on a strong and powerful global handwriting model. A entire text line is considered as an indivisible(More)
In this communication we propose an approach for the writer verification task. The difficulty of this task lies first, in the decision making between the two assumptions which model the problem: "Do the two writings come from the same writer ?" or "Do the two writings come from different writers?" and second, in the evaluation of the error risk associated(More)
This paper deals with the problem of oo-line handwritten text recognition. It presents a system of text recognition that exploits an original principle of adaptation to the handwriting to be recognized. The adaptation principle is based on the automatic learning, during the recognition, of the graphical characteristics of the handwriting. This on-line(More)