Multi-script handwritten signature (Roman & Devanagari)

Citation Author(s):
Obaidullah
Sk
Aliah University
Mridul
Ghosh
Aliah University
Himadri
Mukherjee
New York University ABD
Kaushik
Roy
West Bengal State University
Umapada
Pal
Indian Statistical Institute Kolkata
Submitted by:
Obaidullah Sk
Last updated:
Thu, 05/13/2021 - 00:56
DOI:
10.21227/bgmm-t264
Data Format:
License:
0
0 ratings - Please login to submit your rating.

Abstract 

An offline handwritten signature dataset from two most popular scripts in India namely Roman and Devanagari is proposed here. 

Instructions: 

Writer identification dataset availability on Indic scripts is a major issue to carry forward research in this domain. Devanagari and Roman are two most popular and widely used scripts of India. We have a total of 5433 signatures of 126 writers, out of which 3929 signatures from 80 writers in Roman script and 1504 signatures from 46 writers in Devanagari scripts. Script-wise per writer 49 signatures from Roman and 32 signatures from Devanagari are considered making an average of 43 signatures per writer on whole dataset. We have reported a benchmark results on this dataset for writer identification task using a lightweight CNN architecture. Our proposed method is compared with state-of-the-art handcrafted feature based method such as gray level co-occurrence matrix (GLCM), Zernike moments, histogram of oriented gradients (HOG), local binary pattern (LBP), weber local descriptor (WLD), gabor wavelet transform (GWT) and it outperforms. In addition, few well known CNN arechitechture is also compared with the proposed method and it shows comparable performance. 

User guidance: The images are available in .jpg format with 24 bit color. The dataset is freely available for research work. Cite the following paper while using the dataset

Sk Md Obaidullah, Mridul Ghosh, Himadri Mukherjee, Kaushik Roy and Umapada Pal “Automatic Signature-based Writer Identification in Mixed-script Scenarios”, in 16th International Conference on Document Analysis and Recognition (ICDAR 2021), Lussane, Switzerland, 2021