Optical Character Recognition for Printed Javanese Script Using Projection Profile Segmentation and Nearest Centroid Classifier

Mahastama, Aditya Wikan; Krisnawati, Lucia Dwi

doi:10.1109/accc51160.2020.9347895

Cited by 5 publications

(6 citation statements)

References 9 publications

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“…Projection profiles are generally used for image segmentation. This method has been widely used by previous researchers for line segmentation [22], [23]. The projection profile performs statistical calculations on object pixels according to the horizontal and vertical directions.…”

Section: Profile Projection Histogram (Pph)mentioning

confidence: 99%

Neural Network Classification in Javanese Handwriting Recognition using Projection Profile Histogram and Local Binary Pattern Histogram

Fetty Tri Anggraeny,

Yisti Vita Via,

Retno Mumpuni

et al. 2023

Technium

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Indonesia consists of various regional tribes, where each tribe has cultural diversity and some even have their own regional letters, like Javanese tribe has Javanese characters. Javanese letters consist of 20 basic letters called Nglegena script. Subject about Javanese language is delivered to elementary student until now aims to preserve Indonesian culture especially the Javanese. In this study, we present two feature extraction methods are Local Binary Pattern (LBP) and Profile Projection (PP). Neural Network (NN) chosen as classifiers for classifying 20 javanese letters Nglegena. Some digital image processing processes are carried out, are image inversion, dilation, denoising and skeletoning. The Javanese script dataset is taken from the Kaggle database with the name Aksara Jawa: Aksara Jawa Custom Dataset, consists of 2154 train images and 480 test images. The experiment were carried out in two models, Projection Profile Histogram - Neural Network (PPH-NN) and Local Binary Pattern Histogram - Neural Network (LBPH-NN). The experiment show that both feature extraction methods have very good performance, 99.98% PPH-NN and 89.6% LBPH-NN on average.

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Section: Profile Projection Histogram (Pph)mentioning

confidence: 99%

Neural Network Classification in Javanese Handwriting Recognition using Projection Profile Histogram and Local Binary Pattern Histogram

Fetty Tri Anggraeny,

Yisti Vita Via,

Retno Mumpuni

et al. 2023

Technium

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“…Preservation activities can be preventive, namely preventive activities with the aim of extending a document's life. This activity is in the form of maintaining, caring for, monitoring periodically, and preventing physical damage caused by chemical, biological factors, and so on, as well as curative preservation, namely activities to restore damaged documents to be good again, repairs, deacidification functions, lamination and so on (Kuswara, 2018;Mahastama, 2020).…”

Section: Introductionmentioning

confidence: 99%

Preservation of the Ancient Manuscripts of the Book “Waruga Jagat” and the Book “Babad Sumedang” at the Sumedang Pangeran Foundation Library

2023

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The Pangeran Sumedang Foundation Library is one of the libraries that stores several ancient manuscripts. Ancient manuscripts are a rare type of collection that not everyone, every region, or every library has. Therefore, the existence of a collection of ancient manuscripts needs to be maintained and maintained so that they can be used as a source of information for people who need it. This study aimed to determine preventive and curative ancient manuscript preservation policies and constraints in carrying out ancient manuscript preservation activities at the Pangeran Sumedang Foundation library. While this research tries to examine the preservation of ancient manuscripts, especially the ancient manuscripts of the Waruga Jagat book and the Sumedang chronicle book. The research approach used is a qualitative approach with a case study type. Based on the results of the research, it is known that the birth of an ancient manuscript preservation policy, especially the ancient manuscript of Kita Waruga Jagat and the Sumedang Chronicle book, is because these two ancient manuscripts have very important informational and historical values regarding the founding of the Sumedang Larang Kingdom. Meanwhile, on the other hand, the manuscript is hundreds of years old and its condition has started to deteriorate, so it is necessary to carry out preservation activities. This preservation activity is carried out using prevention and repair or through a transfer of media forms. Regarding the obstacles in this preservation activity, namely budgetary constraints, limited human resources and limited facilities that support ancient manuscript preservation activities.

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“…Other researchers explored methods for developing an OCR system for Sanskrit Manuscripts, using conventional feature extraction, heuristic methods, and machine learning approaches such as CNN, LSTM, or Bidirectional LSTM [11]. An OCR system for Javanese Script was created using Projection Profile Segmentation and Nearest Centroid Classifier, achieving a 93.88% success rate for line segmentation and 73.59% success rate for character segmentation, with a classification accuracy of 60.6% [12]. The Faster Region-based Convolutional Neural Network (Faster R-CNN) has also been employed in Javanese script detection, achieving accuracy ranging from 41.67% to 96.31% [13].…”

Section: Introductionmentioning

confidence: 99%

YOLOv5 and U-Net-based Character Detection for Nusantara Script

Prasetiadi,

Saputra,

Kresna

et al. 2023

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Indonesia boasts a diverse range of indigenous scripts, called Nusantara scripts, which encompass Bali, Batak, Bugis, Javanese, Kawi, Kerinci, Lampung, Pallava, Rejang, and Sundanese scripts. However, prevailing character detection techniques predominantly cater to Latin or Chinese scripts. In an extension of our prior work, which concentrated on the classification of script types and character recognition within Nusantara script systems, this study advances our research by integrating object detection techniques, employing the YOLOv5 model, and enhancing performance through the incorporation of the U-Net model to facilitate the pinpointing of fundamental Nusantara script's character locations within input document images. Subsequently, our investigation delves into rearranging these character positions in alignment with the distinctive styles of Nusantara scripts. Experimental results reveal YOLOv5's performance, yielding a loss rate of approximately 0.05 in character location detection. Concurrently, the U-Net model exhibits an accuracy ranging from 75% to 90% for predicting character regions. While YOLOv5 may not achieve flawless detection of all Nusantara scripts, integrating the U-Net model significantly enhances the detection rate by 1.2%.

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Optical Character Recognition for Printed Javanese Script Using Projection Profile Segmentation and Nearest Centroid Classifier

Cited by 5 publications

References 9 publications

Neural Network Classification in Javanese Handwriting Recognition using Projection Profile Histogram and Local Binary Pattern Histogram

Neural Network Classification in Javanese Handwriting Recognition using Projection Profile Histogram and Local Binary Pattern Histogram

Preservation of the Ancient Manuscripts of the Book “Waruga Jagat” and the Book “Babad Sumedang” at the Sumedang Pangeran Foundation Library

YOLOv5 and U-Net-based Character Detection for Nusantara Script

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