Preprocessing Model of Manuscripts in Javanese Characters

Widiarti, Anastasia Rita; Harjoko, Agus; Marsono,; Hartati, Sri

doi:10.4236/jsip.2014.54014

Cited by 6 publications

(7 citation statements)

References 6 publications

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“…In our experiment, the implementation using projection profile based on [6], [7] has the result 71.8% of segmentation accuracy. There are many errors occurs in overlap line or character.…”

Section: Introductionmentioning

confidence: 81%

“…Several studies on the character segmentation of Javanese characters have been automatically carried out. Himamunanto [6] and Widiarti [7] apply the projection profile for line segmentation and character segmentation in Javanese script documents. The method was implemented on 87 pages of Hamong Tani document images which are handwritten documents of Javanese script.…”

Section: Introductionmentioning

confidence: 99%

“…Improvement of segmentation results to 90.36% is obtained by applying binarization and removing noise to improve image conditions before segmentation. Besides that, the tilt of the character image on the document is also carried out when character segmentation process and combination with the connected component method to separate overlapping characters [7].…”

Section: Introductionmentioning

confidence: 99%

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Character Image Segmentation of Javanese Script Using Connected Component Method

Sugianela

Suciati²

2019

Jurnal Ilmu Komputer dan Informasi

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Automation of Javanese script translation is needed to make it easier for people to understand the meaning of ancient Javanese script. By using Javanese script image as input, the translation system generally consists of character segmentation, character recognition, and combining the recognized characters as a meaningful word. The segmentation which obtains region of interest of each character, is an important process in the translation system. In the previous research, segmentation using projection profile method can separate each character well. The method can overcome characters overlapping, but it still produces truncated characters. In this study, we proposed a new segmentation to reduce the truncated character. The first step of the proposed method is pre-processing that consists of converting input into binary image and cleaning noises. The next step is to determine the connected component labels, which further perform as candidate of characters. Some of the candidates are still represented by more than one labels, so that we need a process to merge the connected component labels that have centroid distance less than threshold. We evaluate the proposed method using Intersection over Union (IoU). The evaluation shows the best accuracy 93,26%.

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“…In our experiment, the implementation using projection profile based on [6], [7] has the result 71.8% of segmentation accuracy. There are many errors occurs in overlap line or character.…”

Section: Introductionmentioning

confidence: 81%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Character Image Segmentation of Javanese Script Using Connected Component Method

Sugianela

Suciati²

2019

Jurnal Ilmu Komputer dan Informasi

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“…Widiarti, et al [3] conducted research on preprocessing for Javanese manuscripts. Preprocessing steps carried out are binarization, noise reduction, line segmentation, and character segmentation.…”

Section: Introductionmentioning

confidence: 99%

Automatic Recognition of the NIK in Electronic KTP

Mirah¹,

Widiarti

2019

Proceedings of the International Conference of Science and Technology for the Internet of Things

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Automatic recognition of NIK is a process carried out to perform numeric character recognition stages. The purpose of automatic recognition of NIK is to produce a text that contains images by processing E-KTP images. The preprocessing stage has 4 steps, namely resizing, grayscaling, binarization and segmentation. The segmentation method which is the projection profile. The extraction phase uses invariant moments of HU and Intensity of Character (IoC). The accuracy of our results obtained for the segmentation process is 98.57% of 70 E-KTP data. The accuracy of our results obtained to reach the peak has a value of 1-fold of 86.96%, a 2-fold of 91.30% and a 3-fold of 78.26%.

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“…The process of OCR involves several steps including segmentation, feature extraction, and classification. Some applications of OCR range from people wish to scan in a document and have the text of that document available in a word processor, to recognition of license plate numbers and zip codes [1]- [5].…”

Section: Introductionmentioning

confidence: 99%

Improving the OCR of Low Contrast, Small Fonts, Dark Background Forms Using Correlated Zoom and Resolution Technique (CZRT)

Iskandarani¹

2015

JDAIP

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Many formal institutions, companies, hospitals, laboratories need some time to exchange hand signed reports through modern communication means such as Fax, E-mails, and others. A problem is faced due to the quality of both scanned documents and originally used paper, which results in problems in converting such images to text. In addition, font type and size, contrast and background darkness have an adverse effect on the accuracy of the resulted text. Thus, an investigation into the relationship between scanned document zoom and scanning resolution in Dots per Inch (DPI) for a special case and type of scanned forms is carried out to enable design of an algorithm that takes into account such cases. It is found that a much higher level of zooming and resolution is needed to achieve acceptable recognition for the special case of dark, low contrast, small font forms. It is also found that the optimum zooming level is set by the number of recognized words as they are more difficult to learn and analyze.

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Preprocessing Model of Manuscripts in Javanese Characters

Cited by 6 publications

References 6 publications

Character Image Segmentation of Javanese Script Using Connected Component Method

Character Image Segmentation of Javanese Script Using Connected Component Method

Automatic Recognition of the NIK in Electronic KTP

Improving the OCR of Low Contrast, Small Fonts, Dark Background Forms Using Correlated Zoom and Resolution Technique (CZRT)

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