Text Detection In Indonesian Identity Card Based On Maximally Stable Extremal Regions
Most of Indonesian organizations either it is government or non government sometime required their member to provide their identity card (E-KTP) as legal document collection in their database. This collection of image usually being used as manual verification method. These document images acquired b...
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| Main Authors: | , , |
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| Format: | EJournal Article |
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IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.,
2019-04-30.
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| Online Access: | Get Fulltext Get Fulltext |
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| LEADER | 02578 am a22003133u 4500 | ||
|---|---|---|---|
| 001 | IJCSS_41259 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Purba, Angga Maulana |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Harjoko, Agus |e author |
| 700 | 1 | 0 | |a Wibowo, Mohammad Edi |e author |
| 245 | 0 | 0 | |a Text Detection In Indonesian Identity Card Based On Maximally Stable Extremal Regions |
| 260 | |b IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia., |c 2019-04-30. | ||
| 500 | |a https://jurnal.ugm.ac.id/ijccs/article/view/41259 | ||
| 520 | |a Most of Indonesian organizations either it is government or non government sometime required their member to provide their identity card (E-KTP) as legal document collection in their database. This collection of image usually being used as manual verification method. These document images acquired by each person with their own device, there are variations of angles they are used to acquire the image. This situation created problems in text recognition by OCR softwares especially in text detection part, orientation and noise will affect their accuracy. These cases making the text detection more complex and cannot be solved by simple vertical projection profile of black pixels. This research proposed a method to improve text detection in identity document by fixing the orientation first, then using MSER regions to form text region. We fix the orientation using the line that made by Progressive Probabilistic Hough Transform. Then we used MSER to obtain all candidate regions and Horizontal RLSA acts as connector between those candidate. The orientation fixing strategy reach average of margin error 0.377o (in 360o system) and the text detection method reach 84.49% accuracy in best condition. | ||
| 540 | |a Copyright (c) 2019 IJCCS (Indonesian Journal of Computing and Cybernetics Systems) | ||
| 540 | |a http://creativecommons.org/licenses/by-sa/4.0 | ||
| 546 | |a eng | ||
| 690 | |a Computer Science | ||
| 690 | |a MSER, Hough Transform; Progressive Probabilistic Hough Transform; RLSA; text detection | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 13, No 2 (2019): April; 177-188 | |
| 786 | 0 | |n 2460-7258 | |
| 786 | 0 | |n 1978-1520 | |
| 787 | 0 | |n https://jurnal.ugm.ac.id/ijccs/article/view/41259/24373 | |
| 856 | 4 | 1 | |u https://jurnal.ugm.ac.id/ijccs/article/view/41259 |z Get Fulltext |
| 856 | 4 | 1 | |u https://jurnal.ugm.ac.id/ijccs/article/view/41259/24373 |z Get Fulltext |