Writer identification using VLAD encoded contour-Zernike moments

Local feature descriptors in combination with bag of (visual) words have recently become the state of the art in writer identification. In this work we propose the use of Zernike moments evaluated at the contours of the script as local descriptor. We then form a global descriptor by encoding the extracted Zernike moments into Vectors of Locally Aggregated Descriptors (VLAD). This local / global descriptor combination outperforms existing methods: on the ICDAR 2013 benchmark database our Zernike / VLAD method yields 0.880 mAP, a 31% improvement over the 0.671 mAP of the state of the art. We also set a new performance standard on the CVL dataset.