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- W2807440061 abstract "Writer Identification aims to identify a certain writer from a given group of candidates by their handwriting. Although it is very significant in security systems like bank account verification systems, existing works focus on document-level or text-level writer identification. This limits their scalabilities and flexibilities in realistic scenarios as they require complete document or text. To facilitate the realistic applications of writer identification, we propose a novel technology, letter-level writer identification, which requires only a few letters as the identification cue. It is challenging due to large intra-class discrepancy and implicit identifiable writing cues. Considering these challenges, we propose a novel deep model called Multi-Branch Encoding net (Mul-BEnc). To evaluate our model and provide a benchmark for this problem, we have collected a large Letter-stroke sequence Writer identification DataBase (LetWriterDB). The experimental results validate the effectiveness of our model." @default.
- W2807440061 created "2018-06-13" @default.
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- W2807440061 date "2018-05-01" @default.
- W2807440061 modified "2023-09-26" @default.
- W2807440061 title "Letter-Level Writer Identification" @default.
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- W2807440061 doi "https://doi.org/10.1109/fg.2018.00061" @default.
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