Comparing Feature Matching Methods to Identify Persian Writers

This study explores a comprehensive set of feature matching techniques to address the challenge of writer identification in Persian handwritten scripts. Writer identification, a key task in the domain of document analysis and forensic handwriting verification, has seen increasing use of local feature descriptors due to their robustness to scale, rotation, and noise. Although the literature highlights the potential of such techniques, limited comparative research has been conducted specifically for Persian script. In this work, we implement and evaluate several well-known feature matching algorithms including SIFT, SURF, BRISK, FREAK, and Harris corner detector-based hybrids such as Harris-SURF, Harris-FREAK, and Harris-BRISK as well as combinations like BRISK-SURF, SURF-FREAK, and SURF-BRISK. The writer identification process is carried out by comparing the feature points in a query image against those in a set of reference images. The reference image that exhibits the highest number of correctly matched keypoints is identified as belonging to the same writer as the query sample. Our experimental findings reveal that among the evaluated algorithms, the SIFT and SURF methods outperform others in terms of accuracy and reliability in identifying Persian writers. Nevertheless, several hybrid approaches also produce promising results, suggesting that combining feature detectors and descriptors can offer valuable performance improvements. This study provides a foundation for future research and applications in Persian handwriting analysis and biometric authentication.

کلیدواژه ها:

Writer Identification ، Feature matching ، SIFT algorithm ، SURF Algorithm ، FREAK Algorithm ، Harris Method