Writer Identification using Deep Learning with FAST Keypoints and Harris corner detector

Self-Writer: Clusterable Embedding Based Self-Supervised Writer Recognition from Unlabeled Data

“…Their framework outperforms the handcrafted feature-based and CNN-based techniques due to its robustness. In [31], Semma et al employ FAST key points and the Harris corner detector to identify points of interest in the handwriting and extract key points from handwriting and feeding small patches around these key points to a CNN for feature learning and classification. Xing et al [6] proposed DeepWriter, a textindependent writer recognition based on a deep, multistream CNN.…”

Writer identification using textural features

“…In the last decade, particular attention has been given to the use of deep learning in the area of writer identification. The work based on deep learning [5,16,8,13,3,12] is mainly based on the learning of information contained in fragments of images of small or medium sizes. Two main approaches have been adopted based either on an end-to-end classification of the last layer of convolutional networks [8] or on the encoding of a given layer using known encoding methods like VLAD and Triangulation Embedding [13,3].…”

Writer identification using VLAD Encoding of the Histogram of Gradient Angle Distribution

“…Writer recognition seeks to identify the author of a questioned document according to several parameters based on his handwriting style. The work carried out made it possible to cover several languages such as Arabic [4], English [25], German [10], French [6], Portuguese [5] and Chinese [28]. A wide variety of systems have been proposed for writer recognition, which can be divided into two groups: structure-based and texture-based systems.…”

“…In addition to the systems mentioned above, several methods have been proposed based on other local descriptors, such as Run length [12], edge-hinge [12], edge-direction [12], Contour-hinge [8], Contourdirection [8], CLBP [1], VLBP [1] ... The introduction of neural networks has made it possible to achieve good performance [24,25,13,27,10,18]. Methods based on deep learning generally employ two classification approaches.…”

“…An end-to-end approach [18] that is based on CNN's last layer classification. While in the second approach, local descriptors extracted from a given layer are introduced directly into a classifier [13] or encoded using VLAD, Triangulation Embedding or Fisher Vector encoding techniques [25,10] to have global descriptors.…”