Thai Automatic signature verification System Employing Textural Features

Abstract

This study focuses on a comprehensive study of Automatic Signature Verification (ASV) for off-line Thai signatures; an investigation was carried out to characterise the challenges in Thai ASV and to baseline the performance of Thai ASV employing baseline features, being Local Binary Pattern, Local Directional Pattern, Local Binary and Directional Patterns combined (LBDP), and the baseline shape/feature-based hidden Markov model. As there was no publicly available Thai signature database found in the literature, the authors have developed and proposed a database considering real-world signatures from Thailand. The authors have also identified their latent challenges and characterised Thai signature-based ASV. The database consists of 5,400 signatures from 100 signers. Thai signatures could be bi-script in nature, considering the fact that a single signature can contain only Thai or Roman characters or contain both Roman and Thai, which poses an interesting challenge for script-independent SV. Therefore, along with the baseline experiments, the investigation on the influence and nature of bi-script ASV was also conducted. From the equal error rates and Bhattacharyya distance, the score achieved in the experiments indicate that the Thai SV scenario is a script-independent problem. The open research area on this subject of research has also been addressed.

Explainable Offline Automatic Signature Verifier to Support Forensic Handwriting Examiners

“…We estimate a weak performance of 2.16 and 2.48 perceptual points for RF and SF, respectively, for the most challenging system in the Thai database. Nevertheless, it is worth pointing out that our system outperformed the results given in [12]. In the case of BiosecurID, we lost about 3.62 and 4.73 of performance for RF and SF.…”

“…The motivation is to study our system in these two situations, the actual use of which would depend on the real application. The experiments were conducted with offline specimens available in the MCYT-75 [57], BiosecurID [24], Thai [12], and CEDAR [37] databases. Please note that all these databases are publicly available and allow experiments using two scripts.…”

One vs. One Offline Signature Verification: A Forensic Handwriting Examiners Perspective

“…The motivation is to study our system in these two situations, the actual use of which would depend on the real application. The experiments were conducted with offline specimens available in the MCYT-75 [57], BiosecurID [24], Thai [12], and CEDAR [37] databases. Please note that all these databases are publicly available and allow experiments using two scripts.…”

ICFHR 2020 Competition on Short answer ASsessment and Thai student SIGnature and Name COMponents Recognition and Verification (SASIGCOM 2020)

“…Figure 1 displays some examples of handwritten short answers. For Thai student signatures dataset [12], both Genuine Signature (GS) and forged Thai student signatures were obtained from 100 students. As a result, the whole dataset contains 3,000 (100 signer's × 30 times) genuine signatures in this dataset.…”

“…For signature and name component verification tasks, the verifiers were trained with the first five (5) genuine samples of each writer/signer of the corresponding datasets for repeatability of the experiments and in [12,13]. The rest of the genuine samples were used for testing the false rejection rate.…”