Grayification: A meaningful grayscale conversion to improve handwritten historical documents analysis

Abstract This paper presents an improvement of handwriting binarization techniques on colored historical documents. We introduce a novel preprocessing step into the usual document image analysis (DIA) workflow. Before binarization, we propose a grayification step to enhance the input image with the help of a new grayscale conversion algorithm, namely the grayification algorithm. This new algorithm uses luminance and color information to improve the contrast between the foreground and the background. Especially on documents with non-black ink and moreover with diverse colors, e.g., illuminations in historical manuscripts, we expect an increased performance. The binarization give then better results on this enhanced grayscale image, and in particular color text is binarized as well as black text. In fact, by adding a preprocessing step to enhance the input grayscale image, the results on all the following tasks of the analysis chain should be improved. This modification of the usual workflow of historical document analysis eases the binarization task as well as other following tasks like layout analysis, line segmentation, OCR, etc. We demonstrate the effects of our novel preprocessing technique on a set of challenging historical documents, which we make publicly available for research purpose, and two publicly available datasets. This improvement is illustrated in this paper on the binarization task, where the results of four different binarization methods are successfully improved.