{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,2]],"date-time":"2025-11-02T03:50:55Z","timestamp":1762055455390,"version":"build-2065373602"},"reference-count":28,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,6,9]],"date-time":"2022-06-09T00:00:00Z","timestamp":1654732800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["61862061","61563052","61163028","BS180268","XJEDU2017TO02"],"award-info":[{"award-number":["61862061","61563052","61163028","BS180268","XJEDU2017TO02"]}]},{"name":"Scientific Research Initiate Program of Doctors of Xinjiang University","award":["61862061","61563052","61163028","BS180268","XJEDU2017TO02"],"award-info":[{"award-number":["61862061","61563052","61163028","BS180268","XJEDU2017TO02"]}]},{"name":"Funds for Creative Groups of Higher Educational Research Plan in Xinjiang Uyghur Autonomous, China","award":["61862061","61563052","61163028","BS180268","XJEDU2017TO02"],"award-info":[{"award-number":["61862061","61563052","61163028","BS180268","XJEDU2017TO02"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>To further improve the accuracy of multilingual off-line handwritten signature verification, this paper studies the off-line handwritten signature verification of monolingual and multilingual mixture and proposes an improved verification network (IDN), which adopts user-independent (WI) handwritten signature verification, to determine the true signature or false signature. The IDN model contains four neural network streams with shared weights, of which two receiving the original signature images are the discriminative streams, and the other two streams are the reverse stream of the gray inversion image. The enhanced spatial attention models connect the discriminative streams and reverse flow to realize message propagation. The IDN model uses the channel attention mechanism (SE) and the improved spatial attention module (ESA) to propose the effective feature information of signature verification. Since there is no suitable multilingual signature data set, this paper collects two language data sets (Chinese and Uyghur), including 100,000 signatures of 200 people. Our method is tested on the self-built data set and the public data sets of Bengali (BHsig-B) and Hindi (BHsig-H). The method proposed in this paper has the highest discrimination rate of FRR of 10.5%, FAR of 2.06%, and ACC of 96.33% for the mixture of two languages.<\/jats:p>","DOI":"10.3390\/info13060293","type":"journal-article","created":{"date-parts":[[2022,6,10]],"date-time":"2022-06-10T02:25:33Z","timestamp":1654827933000},"page":"293","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Multilingual Offline Signature Verification Based on Improved Inverse Discriminator Network"],"prefix":"10.3390","volume":"13","author":[{"given":"Nurbiya","family":"Xamxidin","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Xinjiang University, Urumqi 830046, China"}]},{"family":"Mahpirat","sequence":"additional","affiliation":[{"name":"Educational Administration Department, Xinjiang University, Urumqi 830046, China"}]},{"given":"Zhixi","family":"Yao","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Xinjiang University, Urumqi 830046, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5464-0594","authenticated-orcid":false,"given":"Alimjan","family":"Aysa","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Xinjiang University, Urumqi 830046, China"}]},{"given":"Kurban","family":"Ubul","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Xinjiang University, Urumqi 830046, China"},{"name":"Xinjiang Multilingual Information Technology Key Laboratory, Urumqi 830046, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Eltrabelsi, J., and Lawgali, A. (2021, January 11\u201313). Offline Handwritten Signature Recognition based on Discrete Cosine Transform and Artificial Neural Network. Proceedings of the 7th International Conference on Engineering & MIS 2021, Almaty, Kazakhstan.","DOI":"10.1145\/3492547.3492607"},{"key":"ref_2","first-page":"1","article-title":"One-Class Writer-Independent Offline Signature Verification Using Feature Dissimilarity Thresholding","volume":"11","author":"Hamadene","year":"2017","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Souza, V.L.F., Oliveira, A.L.I., and Cruz, R.M.O. (2021, January 10\u201315). An Investigation of Feature Selection and Transfer Learning for Writer-Independent Offline Handwritten Signature Verification. Proceedings of the 2020 25th IEEE International Conference on Pattern Recognition (ICPR), Milan, Italy.","DOI":"10.1109\/ICPR48806.2021.9413073"},{"key":"ref_4","first-page":"855","article-title":"Offline Handwritten Signature Verification based on Local Ridges Features and Haar Wavelet Transform","volume":"63","year":"2022","journal-title":"Iraqi J. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Batool, F.E., Attique, M., Sharif, M., Javed, K., Nazir, M., Abbasi, A.A., Iqbal, Z., and Riaz, N. (2020). Offline Signature Verification System: A Novel Technique of Fusion of GLCM and Geometric Features using SVM. Multimed Tools Appl., 1\u201320.","DOI":"10.1007\/s11042-020-08851-4"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ajij, M., Pratihar, S., Nayak, S.R., Hanne, T., and Roy, D.S. (2021). Off-line Signature Verification using Elementary Combinations of Directional Codes from Boundary Pixels. Neural Comput. Appl., 1\u201318.","DOI":"10.1007\/s00521-021-05854-6"},{"key":"ref_7","first-page":"252","article-title":"Multiple Classifier System for Writer Independent Offline Handwritten Signature Verification using Hybrid Features","volume":"10","author":"Kumar","year":"2018","journal-title":"Int. J. Comput. Inf. Syst. Ind. Manag. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Arab, N., Nemmour, H., and Chibani, Y. (2019, January 19\u201321). New Local Difference Feature for Off-Line Handwritten Signature Verification. Proceedings of the 2019 IEEE International Conference on Advanced Electrical Engineering (ICAEE), Algiers, Algeria.","DOI":"10.1109\/ICAEE47123.2019.9014828"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"108009","DOI":"10.1016\/j.patcog.2021.108009","article-title":"Offline Signature Verification using a Region-based Deep Metric Learning Network","volume":"118","author":"Liu","year":"2021","journal-title":"Pattern Recognit."},{"key":"ref_10","first-page":"1279","article-title":"Signature Warping and Greedy Approach based Offline Signature Verification","volume":"13","author":"Natarajan","year":"2021","journal-title":"Int. J. Inf. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.9734\/jamcs\/2019\/v32i230141","article-title":"An Improved Geo-Textural based Feature Extraction Vector for Offline Signature Verification","volume":"32","author":"Gyimah","year":"2019","journal-title":"J. Adv. Math. Comput. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"03010","DOI":"10.1051\/itmconf\/20214003010","article-title":"Offline Handwritten Signature Verification using Various Machine Learning Algorithms","volume":"Volume 40","author":"Lokarkare","year":"2021","journal-title":"Proceedings of the ITM Web of Conferences"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.eswa.2019.03.040","article-title":"Multi-Representational Learning for Offline Signature Verification using Multi-Loss Snapshot Ensemble of CNNs","volume":"133","author":"Masoudnia","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhang, S.J., Aysa, Y., and Ubul, K. (2018, January 11\u201312). BOVW based Feature Selection for Uyghur Offline Signature Verification. Proceedings of the Chinese Conference on Biometric Recognition, Urumqi, China.","DOI":"10.1007\/978-3-319-97909-0_74"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Gupta, Y., Kulkarni, S., and Jain, P. (2022, January 11\u201312). Handwritten Signature Verification Using Transfer Learning and Data Augmentation. Proceedings of the International Conference on Intelligent Cyber-Physical Systems, Coimbatore, India.","DOI":"10.1007\/978-981-16-7136-4_19"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wei, P., Li, H., and Hu, P. (2019, January 16\u201317). Inverse Discriminative Networks for Handwritten Signature Verification. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00591"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1115","DOI":"10.1049\/ipr2.12090","article-title":"Identification of Plant Disease Images via a Squeeze-and-Excitation MobileNet Model and Twice Transfer Learning","volume":"15","author":"Chen","year":"2021","journal-title":"IET Image Process."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Hagstr\u00f6m, A.L., Stanikzai, R., and Bigun, J. (2022). Writer Recognition using Off-line Handwritten Single Block Characters. arXiv.","DOI":"10.1109\/IWBF55382.2022.9794466"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Kancharla, K., Kamble, V., and Kapoor, M. (2018, January 28\u201329). Handwritten Signature Recognition: A Convolutional Neural Network Approach. Proceedings of the 2018 International Conference on Advanced Computation and Telecommunication (ICAC), Bhopal, India.","DOI":"10.1109\/ICACAT.2018.8933575"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Liu, J., Zhang, W., and Tang, Y. (2020, January 13\u201319). Residual Feature Aggregation Network for Image Super-Resolution. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00243"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wencheng, C., Xiaopeng, G., and Hong, S. (2017, January 17\u201318). Offline Chinese Signature Verification based on AlexNet. Proceedings of the International Conference on Advanced Hybrid Information Processing, Harbin, China.","DOI":"10.1007\/978-3-319-73317-3_5"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Bonde, S.V., Narwade, P., and Sawant, R. (2020, January 5\u20137). Offline Signature Verification using Convolutional Neural Network. Proceedings of the 2020 6th IEEE International Conference on Signal Processing and Communication (ICSC), Noida, India.","DOI":"10.1109\/ICSC48311.2020.9182727"},{"key":"ref_23","first-page":"1195","article-title":"Uyghur Signature Authentication based on Feature Weighted Fusion of Gray Level Co-occurrence Matrix","volume":"39","author":"Aini","year":"2018","journal-title":"Comput. Eng. Des."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"121","DOI":"10.12677\/JISP.2017.62015","article-title":"Uyghur Off-Line Signature Verification based on the Directional Features","volume":"06","author":"Ghaniheni","year":"2017","journal-title":"J. Image Signal Process."},{"key":"ref_25","unstructured":"Dey, S., Dutta, A., and Toledo, J.I. (2017). Signet: Convolutional Siamese Network for Writer Independent Offline Signature Verification. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Dutta, A., Pal, U., and Llad\u00f3s, J. (2016, January 4\u20138). Compact Correlated Features for Writer Independent Signature Verification. Proceedings of the 2016 23rd IEEE international conference on pattern recognition (ICPR), Cancun, Mexico.","DOI":"10.1109\/ICPR.2016.7900163"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Chattopadhyay, S., Manna, S., and Bhattacharya, S. (2022). SURDS: Self-Supervised Attention-guided Reconstruction and Dual Triplet Loss for Writer Independent Offline Signature Verification. arXiv.","DOI":"10.1109\/ICPR56361.2022.9956442"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Manna, S., Chattopadhyay, S., Bhattacharya, S., and Pal, U. (2022). SWIS: Self-Supervised Representation Learning for Writer Independent Offline Signature Verification. arXiv.","DOI":"10.1109\/ICIP46576.2022.9897562"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/13\/6\/293\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:27:07Z","timestamp":1760138827000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/13\/6\/293"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,9]]},"references-count":28,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["info13060293"],"URL":"https:\/\/doi.org\/10.3390\/info13060293","relation":{},"ISSN":["2078-2489"],"issn-type":[{"type":"electronic","value":"2078-2489"}],"subject":[],"published":{"date-parts":[[2022,6,9]]}}}