Offline Bengali writer verification by PDF-CNN and siamese net

Publication Type:
Conference Proceeding
Citation:
Proceedings - 13th IAPR International Workshop on Document Analysis Systems, DAS 2018, 2018, pp. 381 - 386
Issue Date:
2018-06-22
Metrics:
Full metadata record
Files in This Item:
Filename Description Size
DAS_main.pdfAccepted Manuscript version443.08 kB
Adobe PDF
© 2018 IEEE. Automated handwriting analysis is a popular area of research owing to the variation of writing patterns. In this research area, writer verification is one of the most challenging branches, having direct impact on biometrics and forensics. In this paper, we deal with offline writer verification on complex handwriting patterns. Therefore, we choose a relatively complex script, i.e., Indic Abugida script Bengali (or, Bangla) containing more than 250 compound characters. From a handwritten sample, the probability distribution functions (PDFs) of some handcrafted features are obtained and input to a convolutional neural network (CNN). For such a CNN architecture, we coin the term 'PDFCNN', where handcrafted feature PDFs are hybridized with auto-derived CNN features. Such hybrid features are then fed into a Siamese neural network for writer verification. The experiments are performed on a Bengali offline handwritten dataset of 100 writers. Our system achieves encouraging results, which sometimes exceed the results of state-of-The-Art techniques on writer verification.
Please use this identifier to cite or link to this item: