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.