Codebook‐based writer characterization is an effective technique that has been investigated in a number of recent studies on identification and verification of writers. These methods divide a set of writing samples into small units (fragments or graphemes) and cluster these patterns to produce a codebook. Writer of a handwritten sample is then characterized by the probability (distribution) of producing the codebook patterns. In most cases, a small subset of the database under study is employed to produce the codebook while the rest of the database is used in evaluations. This work aims to validate the hypothesis that the codebook simply serves as a representation space to compare different writings and, in most cases, the patterns in the codebook do not significantly influence the identification and verification performance. The hypothesis is validated by generating a number of codebooks using Greek, Arabic and Chinese handwritten samples. Moreover, codebooks using fragments of handwritten music scores, printed text and synthetic data are also investigated. Evaluations on three well‐known handwriting databases (CVL, BFL and IAM) validate the idea that, in general, the codebook patterns do not have a significant impact on characterizing writer from handwriting.

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码本模式对作者识别的影响：一项实验研究

基于密码本的作者表征是一项有效的技术，最近在有关作者识别和验证的许多研究中进行了研究。这些方法将一组书写样本分成小单元（片段或字素），然后将这些模式聚类以生成码本。然后，通过产生码本图案的概率（分布）来表征手写样本的作者。在大多数情况下，研究中的数据库的一小部分用于生成密码本，而数据库的其余部分用于评估。这项工作旨在验证以下假设：密码本只是充当比较不同作品的代表空间，并且在大多数情况下，密码本中的模式不会显着影响识别和验证性能。通过使用希腊文，阿拉伯文和中文手写样本生成大量密码本来验证该假设。此外，还研究了使用手写乐谱，印刷文本和合成数据片段的密码本。对三个著名的笔迹数据库（CVL，BFL和IAM）进行的评估证实了这样的想法，即，通常，码本模式对笔迹特征的识别不会产生重大影响。