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.
Aliakbarzadeh, M., & Razzazi, F. (2020). Online Persian/Arabic Writer Identification using Gated Recurrent Unit Neural Networks. Majlesi Journal of Electrical Engineering, 14, 73-79. https://doi.org/10.29252/MJEE.14.3.9.
Khosravi, S., & Chalechale, A. (2022). Recognition of Persian/Arabic Handwritten Words Using a Combination of Convolutional Neural Networks and Autoencoder (AECNN). Mathematical Problems in Engineering. https://doi.org/10.1155/2022/4241016.
Safarzadeh, V., & Jafarzadeh, P. (2020). Offline Persian Handwriting Recognition with CNN and RNN-CTC. 2020 25th International Computer Conference, Computer Society of Iran (CSICC), 1-10. https://doi.org/10.1109/CSICC49403.2020.9050073.
Kumar, R., Chanda, B., & Sharma, J. D. (2014). A novel sparse model based forensic writer identification. Pattern Recognition Letters, 35, 105–112. doi:10.1016/j.patrec.2013.07.001.
Schomaker, L., Franke, K., & Bulacu, M. (2007). Using codebooks of fragmented connected-component contours in forensic and historic writer identification. Pattern Recognition Letters, 28(6), 719–727. doi:10.1016/j.patrec.2006.08.005.
Said, H. E., Tan, T. N., & Baker, K. D. (2000). Personal identification based on handwriting. Pattern Recognition, 33(1), 149-160.
Hannad, Y., Siddiqi, I., & El Kettani, M. E. Y. (2016). Writer identification using texture descriptors of handwritten fragments. Expert Systems with Applications, 47, 14–22. doi:10.1016/j.eswa.2015.11.002.
Peyvandi, M., & Zahedi, M. (2013). 11th Iranian Conference on Intelligent Systems. (In Persian).
Wu, X., Tang, Y., & Bu, W. (2014). Offline text-independent writer identification based on scale invariant feature transform. IEEE Transactions on Information Forensics and Security, 9(3), 526–536. doi:10.1109/tifs.2014.2301274.
Su, T., Zhang, T., & Guan, D. (2007). Corpus-based HIT-MW database for offline recognition of general-purpose Chinese handwritten text. International Journal on Document Analysis and Recognition, 10(1), 27–38. doi:10.1007/s10032-006-0037-6.
Bulacu, M., Schomaker, L., & Vuurpijl, L. (2005). Writer identification using edge-based directional features. Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings. Presented at the Seventh International Conference on Document Analysis and Recognition, Edinburgh, UK. doi:10.1109/icdar.2003.1227797.
Hassaine, A., & Maadeed, S. A. (2012). ICFHR 2012 competition on writer identification challenge 2: Arabic scripts. 2012 International Conference on Frontiers in Handwriting Recognition. Presented at the 2012 International Conference on Frontiers in Handwriting Recognition (ICFHR), Bari, Italy. doi:10.1109/icfhr.2012.218.
Marti, U.-V., & Bunke, H. (2002). The IAM-database: an English sentence database for offline handwriting recognition. International Journal on Document Analysis and Recognition, 5(1), 39–46. doi:10.1007/s100320200071.
Sharma, M. K., & Dhaka, V. P. (2015). Offline language-free writer identification based on speeded-up robust features. International Journal of Engineering-Transactions A: Basics, 28(7), 984–994.
Harris, C., & Stephens, M. (1988). A combined corner and edge detector. Procedings of the Alvey Vision Conference 1988. Presented at the Alvey Vision Conference 1988, Manchester. doi:10.5244/c.2.23.
Ben-Musa, A. S., Singh, S. K., & Agrawal, P. (2014, July). Object detection and recognition in cluttered scene using Harris Corner Detection. 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT). India. doi:10.1109/iccicct.2014.6992953.
Bhowmik, M. K., Shil, S., & Saha, P. (2013). Feature points extraction of thermal face using Harris interest point detection. Procedia Technology, 10, 724–730. doi:10.1016/j.protcy.2013.12.415.
Rosten, E., & Drummond, T. (2006). Machine learning for high-speed corner detection. In Lecture Notes in Computer Science. Computer Vision – ECCV 2006 (pp. 430–443). doi:10.1007/11744023_34.
Alahi, A., Ortiz, R., & Vandergheynst, P. (2012). FREAK: Fast Retina Keypoint. 2012 IEEE Conference on Computer Vision and Pattern Recognition. Presented at the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI. doi:10.1109/cvpr.2012.6247715.
Leutenegger, S., Chli, M., & Siegwart, R. Y. (2011). BRISK: Binary Robust invariant scalable keypoints. 2011 International Conference on Computer Vision. Presented at the 2011 IEEE International Conference on Computer Vision (ICCV), Barcelona, Spain. doi:10.1109/iccv.2011.6126542.
Keyvanpour, M., Tavoli, R., & Mozafari, S. (2014). Document image retrieval based on keyword spotting using relevance feedback.
Rusinol, M., Aldavert, D., Toledo, R., & Llados, J. (2011). Browsing heterogeneous document collections by a segmentation-free word spotting method. 2011 International Conference on Document Analysis and Recognition. Beijing, China. doi:10.1109/icdar.2011.22.
Madbouly, A. M. M., Wafy, M., & Mostafa, M. S. M. (2015). Performance Assessment of Feature Detector-Descriptor Combination. International Journal of Computer Science Issues (IJCSI), 12(5).
Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE transactions on systems, man, and cybernetics, 9(1), 62-66.
Lam, L., Lee, S.-W., & Suen, C. Y. (1992). Thinning methodologies-a comprehensive survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(9), 869–885. doi:10.1109/34.161346
Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110. doi:10.1023/b:visi.0000029664.99615.94.
Bay, H., Ess, A., Tuytelaars, T., & Van Gool, L. (2008). Speeded-up robust features (SURF). Computer vision and image understanding, 110(3), 346-359.
Javadzadeh, R. (2017). Persian Writer Identification Dataset. figshare.com.california.s/1362d5e928c349ae058a. https://figshare.com/
Vedaldi, A., & Fulkerson, B. (2010, October). VLFeat: An open and portable library of computer vision algorithms. In Proceedings of the 18th ACM international conference on Multimedia (pp. 1469-1472). ACM.
Javadzadeh,R. , Zahmati Iraj,E. and Ghaffary,H. R. (2023). Comparing Feature Matching Methods to Identify Persian Writers. Transactions on Machine Intelligence, 6(1), 16-27. doi: 10.47176/TMI.2023.16
MLA
Javadzadeh,R. , , Zahmati Iraj,E. , and Ghaffary,H. R. . "Comparing Feature Matching Methods to Identify Persian Writers", Transactions on Machine Intelligence, 6, 1, 2023, 16-27. doi: 10.47176/TMI.2023.16
HARVARD
Javadzadeh R., Zahmati Iraj E., Ghaffary H. R. (2023). 'Comparing Feature Matching Methods to Identify Persian Writers', Transactions on Machine Intelligence, 6(1), pp. 16-27. doi: 10.47176/TMI.2023.16
CHICAGO
R. Javadzadeh, E. Zahmati Iraj and H. R. Ghaffary, "Comparing Feature Matching Methods to Identify Persian Writers," Transactions on Machine Intelligence, 6 1 (2023): 16-27, doi: 10.47176/TMI.2023.16
VANCOUVER
Javadzadeh R., Zahmati Iraj E., Ghaffary H. R. Comparing Feature Matching Methods to Identify Persian Writers. Trans. Mach. Intell., 2023; 6(1): 16-27. doi: 10.47176/TMI.2023.16
