Bennour Akram, Djeddi Chawki, Gattal Abdeljalil, Siddiqi Imran, Mekhaznia Tahar
Larbi Tebessi University, Tebessa, Algeria.
Bahria University, Islamabad, Pakistan.
Forensic Sci Int. 2019 Aug;301:91-100. doi: 10.1016/j.forsciint.2019.05.014. Epub 2019 May 22.
Writer characterization from images of handwriting has remained an important research problem in the handwriting recognition community that finds applications in forensics, paleography and neuropsychology. This paper presents a study to evaluate the effectiveness of an implicit shape codebook technique to recognize writer from digitized images of handwriting. The technique relies on identifying the key points in handwriting and clustering the patches around these key points to generate an implicit shape codebook. A writer is then characterized by the probability distribution of producing the codebook patterns. Experiments are carried out in text-dependent as well text-independent mode using the standard BFL and CVL databases of handwriting images. Promising identification and verification performance is reported in a number of interesting experimental scenarios.
从笔迹图像中进行书写者特征分析一直是笔迹识别领域的一个重要研究问题，该领域在法医学、古文字学和神经心理学中都有应用。本文提出了一项研究，以评估一种隐式形状码本技术从数字化笔迹图像中识别书写者的有效性。该技术依赖于识别笔迹中的关键点，并对这些关键点周围的图像块进行聚类，以生成一个隐式形状码本。然后，通过生成码本模式的概率分布来表征书写者。使用标准的BFL和CVL笔迹图像数据库，在文本相关和文本无关模式下进行了实验。在一些有趣的实验场景中报告了有前景的识别和验证性能。
