Writer Identification Using Junction Features

In this chapter, we propose a novel junction detection method in handwritten images, which uses the stroke-length distribution in every direction around a reference point inside the ink of texts. Our proposed junction detection method is simple and efficient, and yields a junction feature in a natural manner, which can be considered as a local descriptor. We apply our proposed junction detector to writer identification by Junclets which is a codebook-based representation trained from the detected junctions. A new challenging data set which contains multiple scripts (English and Chinese) written by the same writers is introduced to evaluate the performance of the proposed junctions for multi-script writer identification. Furthermore, two other common data sets are used to evaluate our junction-based descriptor. The performance of writer identification indicates that junctions are important atomic elements to characterize the writing styles. The proposed junction detector is applicable to both historical documents and modern handwritings, and can be used as well for junction retrieval.