Signature Identification using Digital Image Processing and Machine Learning Methods
Signature is used to legally approve an agreement, treaty, and state administrative activities. Identification of the signature is required to ensure ownership of a signature and to prevent things like forgery from happening to the owner of the signature. In this study, data signatures were obtained...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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P3M Politeknik Negeri Banjarmasin
2023-06-01
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| Series: | Jurnal ELTIKOM: Jurnal Teknik Elektro, Teknologi Informasi dan Komputer |
| Subjects: | |
| Online Access: | https://eltikom.poliban.ac.id/index.php/eltikom/article/view/618 |
| _version_ | 1827912969919397888 |
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| author | I Kadek Nurcahyo Putra Ni Putu Dita Ariani Sukma Dewi Diah Ayu Pusparani Dibi Ngabe Mupu |
| author_facet | I Kadek Nurcahyo Putra Ni Putu Dita Ariani Sukma Dewi Diah Ayu Pusparani Dibi Ngabe Mupu |
| author_sort | I Kadek Nurcahyo Putra |
| collection | DOAJ |
| description | Signature is used to legally approve an agreement, treaty, and state administrative activities. Identification of the signature is required to ensure ownership of a signature and to prevent things like forgery from happening to the owner of the signature. In this study, data signatures were obtained from 25 people over the age of 50. The signers provided 20 signatures and were free to choose the stationery used to write the signature on white paper. The total data obtained in this study was 500 signature data. The obtained signature was scanned to create a signature image, which was then pre-processed to prepare it for feature extraction, which can characterize the signature images. The HOG method was used to extract features, resulting in a dataset with 4,536 feature vectors for each signature image. To identify the signature image, the classification methods SVM, Decision Tree, Nave Bayes, and K-NN were compared. SVM achieved the highest accuracy, which is 100%. When K=5, the K-NN method achieved a fairly good accuracy of 97.3%. Meanwhile, Naive Bayes and Decision Tree achieved accuracy significantly lower than K-NN (61%). Because the HOG method produced a large feature vector for each signature, it is recommended that important features that represent signatures be optimized or extracted to produce smaller features to speed up computation without sacrificing accuracy, and that the HOG method be compared to other extraction feature methods to obtain a better model in future research. |
| first_indexed | 2024-03-13T02:22:48Z |
| format | Article |
| id | doaj.art-2f6e50b5b95c4e78b4f7234809faf2d6 |
| institution | Directory Open Access Journal |
| issn | 2598-3245 2598-3288 |
| language | English |
| last_indexed | 2024-03-13T02:22:48Z |
| publishDate | 2023-06-01 |
| publisher | P3M Politeknik Negeri Banjarmasin |
| record_format | Article |
| series | Jurnal ELTIKOM: Jurnal Teknik Elektro, Teknologi Informasi dan Komputer |
| spelling | doaj.art-2f6e50b5b95c4e78b4f7234809faf2d62023-06-30T07:10:08ZengP3M Politeknik Negeri BanjarmasinJurnal ELTIKOM: Jurnal Teknik Elektro, Teknologi Informasi dan Komputer2598-32452598-32882023-06-0171293710.31961/eltikom.v7i1.618574Signature Identification using Digital Image Processing and Machine Learning MethodsI Kadek Nurcahyo Putra0Ni Putu Dita Ariani Sukma Dewi1Diah Ayu Pusparani2Dibi Ngabe Mupu3Universitas Pendidikan Ganesha, Singaraja, IndonesiaUniversitas Pendidikan Ganesha, Singaraja, IndonesiaUniversitas Pendidikan Ganesha, Singaraja, IndonesiaUniversitas Pendidikan Ganesha, Singaraja, IndonesiaSignature is used to legally approve an agreement, treaty, and state administrative activities. Identification of the signature is required to ensure ownership of a signature and to prevent things like forgery from happening to the owner of the signature. In this study, data signatures were obtained from 25 people over the age of 50. The signers provided 20 signatures and were free to choose the stationery used to write the signature on white paper. The total data obtained in this study was 500 signature data. The obtained signature was scanned to create a signature image, which was then pre-processed to prepare it for feature extraction, which can characterize the signature images. The HOG method was used to extract features, resulting in a dataset with 4,536 feature vectors for each signature image. To identify the signature image, the classification methods SVM, Decision Tree, Nave Bayes, and K-NN were compared. SVM achieved the highest accuracy, which is 100%. When K=5, the K-NN method achieved a fairly good accuracy of 97.3%. Meanwhile, Naive Bayes and Decision Tree achieved accuracy significantly lower than K-NN (61%). Because the HOG method produced a large feature vector for each signature, it is recommended that important features that represent signatures be optimized or extracted to produce smaller features to speed up computation without sacrificing accuracy, and that the HOG method be compared to other extraction feature methods to obtain a better model in future research.https://eltikom.poliban.ac.id/index.php/eltikom/article/view/618hogknnnaive bayessignaturesvm |
| spellingShingle | I Kadek Nurcahyo Putra Ni Putu Dita Ariani Sukma Dewi Diah Ayu Pusparani Dibi Ngabe Mupu Signature Identification using Digital Image Processing and Machine Learning Methods Jurnal ELTIKOM: Jurnal Teknik Elektro, Teknologi Informasi dan Komputer hog knn naive bayes signature svm |
| title | Signature Identification using Digital Image Processing and Machine Learning Methods |
| title_full | Signature Identification using Digital Image Processing and Machine Learning Methods |
| title_fullStr | Signature Identification using Digital Image Processing and Machine Learning Methods |
| title_full_unstemmed | Signature Identification using Digital Image Processing and Machine Learning Methods |
| title_short | Signature Identification using Digital Image Processing and Machine Learning Methods |
| title_sort | signature identification using digital image processing and machine learning methods |
| topic | hog knn naive bayes signature svm |
| url | https://eltikom.poliban.ac.id/index.php/eltikom/article/view/618 |
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