A Novel and Practical System for Verifying Signatures on Persian Handwritten Bank Checks

Abstract

A novel system for verifying signatures on Persian handwritten bank checks is presented, in this paper. The presented system includes two main phases called: training and verification phases. At first, the system is trained using some genuine signatures provided by each customer in training phase. Then verifying the signatures on incoming checks is carried out in the verification phase. Feature extraction step is conducted based on a new approach that uses Multitresolution box-counting (MRBC) method for estimating the fractal dimension of signatures. Here, signature verification is modeled as testing hypothesis, and decision about acceptance or rejection of signatures on incoming checks is carried out using Kolmogorov–Smirnov test. The presented system has been tested on two databases: our new created database and NISDCC database which was used for ICDAR 2009 signature verification competition. Our database has 1000 genuine signatures provided by 100 participants and 200 skilled forgeries copied from genuine samples by five forgers. In total our database includes 1200 Persian signatures. Obtained results show promising performance of the presented system for its application on Persian banks.

Explainable Offline Automatic Signature Verifier to Support Forensic Handwriting Examiners

“…As an illustration, the use of signatures is prevalent in bank checks [23]. The verification of authorship can be open to interpretation by an FHE and may often require justification in a court of law.…”

“…Examines psychological aspects of handwriting to assess authenticity [56] Machine learning Utilizes supervised learning models for signature verification based on training data [23] Local features Identify keypoints or interest points in signatures for robust feature matching. [47] Stroke sequence analysis Studies the order and sequence of strokes in a signature [50] Local binary patterns Encodes texture information for signature representation and comparison [53] Handwriting feature analysis Analyzes individual handwriting features like slant, size, and pressure [68] Graph-based approaches Represent signatures as graphs and analyze structural information for authentication [45] Forensic document analysis Investigates paper, ink, and other physical aspects of the document [55,69] Signature comparison Compares questioned signatures to known reference signatures [17] Biometric authentication Uses biometric data like pen pressure and speed for verification [19] Neural networks Employs deep learning models for complex signature analysis [43,74] signatures of individuals.…”