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Text-independent writer identification is challenging due to the huge variation of written contents and the ambiguous written styles of different writers. This paper proposes DeepWriter, a deep multi-stream CNN to learn deep powerful representation for recognizing writers. DeepWriter takes local handwritten patches as input and is trained with softmax classification loss. The main contributions are:...
In our previous works, a structural pronunciation representation was proposed to extract the linguistic features from dialect pronunciation and classify speakers based on their dialects. In this paper, in order to prove that the structural method can extract the purely speaker-invariant dialectal features, several new experiments are carried out. First, using the data of 19 speakers from different...
This paper proposes a novel framework for offline signature verification. Different from previous methods, our approach makes use of online handwriting instead of handwritten images for registration. The online registrations enable robust recovery of the writing trajectory from an input offline signature and thus allow effective shape matching between registration and verification signatures. In addition,...
This paper addresses the problem to recover drawing order from single-stroked offline handwritten image. The recovery problem is formulated as to find the smoothest path to cover all the edges in the graph representation of an input image. The two main contributions are: (1) we introduce direction context to calculate the smoothness between edges; (2) the smoothest path is found by solving a new graph...
Restoration of writing order from a single-stroked handwriting image can be seen as the problem of finding the smoothest path in its graph representation. In this paper, a 3-phase approach to restore a writing order is proposed within the framework of the edge continuity relation (ECR). In the initial, local phase, in order to obtain possible ECRs at an even-degree node, a neural network is used for...
Dynamic time warping (DTW) has been widely used to align and compare two sequences. DTW can efficiently deal with local warp or deformation between sequences. However, it can't take account of affine transformation of sequences, such as rotation, shift and scale. This paper introduces a novel affine invariant dynamic time warping (AI-DTW) method, which tries to deal with the affine transformation...
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