Discriminatory power of handwritten words for writer recognition

Analysis of allographs (characters) and allograph combinations (words) is the key for the identification/verification of a writer´s handwriting. While allographs are usually part of words and the segmentation of a word into allographs is a subjective process, analysis of handwritten words is a natural option, complementary to allograph and document-level analysis. We consider four different types of features obtained using both segmentation-based and segmentation-free approaches: (i) GSC (gradient, structural and concavity) features that are extracted from the cells of a grid superimposed on the word image (ii) WMR (word model recognizer) features, extracted from the cells of superimposed grids on the segmented characters (iii) SC (shape curvature) features that describe characters by the distribution of curvature values on their contours and (iv) SCON (shape context) features that measure the similarity between character contour shapes. Their individual and accumulated performance is evaluated for the writer identification and verification tasks on over 75000 words images, written by more than 1000 writers. Experimental results show that handwritten words are very effective in discriminating handwriting and that both segmentation-free and segmentation-based approaches are valid.