### Related Work  
Some authors [6; 7; 8; 9; 10; 11; 12; 13] provide a comprehensive exposition on the significance of deep learning techniques in the context of both offline and online signature verification. In their study, [PERSON] et al. [14] proposed an apparatus integrating the outcomes of two distinct platforms. The first solution utilizes video footage as a primary source of information. The handwriting procedure is captured using a web camera, which tracks precise stylus motions to capture the online characteristics of a signature. To verify offline features, signatures from video recordings are scanned and trimmed into picture format for further analysis. Webcams were also used in the research published by [PERSON] et al. [15] as a visual-based online signature verification method. The researchers conducted experiments using a private database including 150 authentic signatures to determine the optimal camera positioning. After that, they compared the suggested system against a database of 390 real signatures and 1560 skillfully forged signatures. Other researchers [16] have devised a unique ballpoint pen equipped with sensors to transmit data streams from tri-axial acceleration, gyroscope, magnetometer, and force sensors. A unique OnHW dataset was generated, presenting a time-series depiction of sensor data capturing the pen motion. [PERSON] et al. [17] also used a smart pen equipped with a camera and pressure sensor in their study. The objective was to sign on a sheet of paper covered in minuscule dots while acquiring the signature's image and trajectory data in real time. However, only Chinese signatures are included in the dataset proposed by the authors. mmSign [18] is an online signature verification system that utilizes millimeter wave (mm-Wave) technology to precisely detect the user's hand movements. SilentSign [19] represents a system architecture that employs the embedded microphone and speaker of smart devices to enhance real-time signature verification. Despite the achieved results, the solution has certain limitations. For example, the performance is reduced when the device undergoes vertical displacement or rotational motion concerning the sensory region. [PERSON] et al. [20] implemented a similar approach by using alterations in auditory waves to achieve signature verification. The researchers used a novel chord-based approach to assess the changes in phase resulting from tiny movements. Furthermore,Wei et al. [21] proposed a system utilizing both vibration and acoustic data to verify signatures. During the signature procedure, the integrated microphone of the tablet was used to capture the sound of the stylus rubbing against the display. Vibration data, derived from the tablet's accelerometer and gyroscope, were used to detect hand motion while signing. The authors observe that the system constraints, such as limited test users and a fixed signature location, reduce the model's verification performance. Smartwatches and fitness trackers include sensors that can be used to verify handwritten signatures on paper documents. The use of a smartwatch was documented in the study conducted by [PERSON] et al. [22]. It was employed to gather motion sensor data in the form of six-dimensional temporal signals, specifically including three-axis gyroscope and three-axis acceleration. A prototype solution, as described by [PERSON] et al. [23], also employs the use of a built-in accelerometer and gyroscope to record motion data. To operate the system, users are required to wear the wrist-worn device on the hand that is considered to be their dominant hand. Furthermore, the signatures were obtained by using a stylus on a tablet device rather than employing a conventional pen and paper. In [24], a tablet computer with a built-in touchscreen sensor was utilized to gather online signatures from 74 individuals. In this research, a database was constructed using signatures collected in uncontrolled mobile conditions. [PERSON] et al.