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: Purba, Angga Maulana (Author), Harjoko, Agus (Author), Wibowo, Mohammad Edi (Author)
Format: EJournal Article
Published: IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia., 2019-04-30.
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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