H-DIBCO 2010 - Handwritten Document Image Binarization Competition

2010 ◽  
Author(s):  
Ioannis Pratikakis ◽  
Basilis Gatos ◽  
Konstantinos Ntirogiannis
Keyword(s):  
Document Image ◽  
Image Binarization ◽  
Document Image Binarization ◽  
Handwritten Document

scholarly journals Kombinasi Metode Nilai Ambang Lokal dan Global untuk Restorasi Dokumen Jawi Kuno

2020 ◽  
Vol 7 (1) ◽  
pp. 163
Author(s):  
Khairun Saddami ◽  
Fitri Arnia ◽  
Yuwaldi Away ◽  
Khairul Munadi
Keyword(s):  
Signal To Noise Ratio ◽  
Document Image ◽  
Image Binarization ◽  
Signal To Noise ◽  
Document Image Binarization ◽  
Handwritten Document ◽  
Global Thresholding ◽  
Noise Ratio ◽  
Binarization Method ◽  
F Measure

<p class="Abstrak">Dokumen Jawi kuno merupakan warisan budaya yang berisi informasi penting tentang peradaban masa lalu yang dapat dijadikan pedoman untuk masa sekarang ini. Dokumen Jawi kuno telah mengalami penurunan kualitas yang disebabkan oleh beberapa faktor seperti kualitas kertas atau karena proses penyimpanan. Penurunan kualitas ini menyebabkan informasi yang terdapat pada dokumen tersebut menghilang dan sulit untuk diakses. Artikel ini mengusulkan metode binerisasi untuk membangkitkan kembali informasi yang terdapat pada dokumen Jawi kuno. Metode usulan merupakan kombinasi antara metode binerisasi berbasis nilai ambang lokal dan global. Metode usulan diuji terhadap dokumen Jawi kuno dan dokumen uji standar yang dikenal dengan nama <em>Handwritten</em> <em>Document Image Binarization Contest</em> (HDIBCO) 2016. Citra hasil binerisasi dievaluasi menggunakan metode: <em>F-measure</em>, <em>pseudo F-measure</em>, <em>peak signal-to-noise ratio</em>, <em>distance reciprocal distortion</em>, dan <em>misclasification penalty metric</em>. Secara rata-rata, nilai evaluasi <em>F-measure</em> dari metode usulan mencapai 88,18 dan 89,04 masing-masing untuk dataset Jawi dan HDIBCO-2016. Hasil ini lebih baik dari metode pembanding yang menunjukkan bahwa metode usulan berhasil meningkatkan kinerja metode binerisasi untuk dataset Jawi dan HDIBCO-2016.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstract"><em>Ancient Jawi document is a cultural heritage, which contains knowledge of past civilization for developing a better future. Ancient Jawi document suffers from severe degradation due to some factors such as paper quality or poor retention process. The degradation reduces information on the document and thus the information is difficult to access. This paper proposed a binarization method for restoring the information from degraded ancient Jawi document. The proposed method combined a local and global thresholding method for extracting the text from the background. The experiment was conducted on ancient Jawi document and Handwritten Document Image Binarization Contest (HDIBCO) 2016 datasets. The result was evaluated using F-measure, pseudo F-measure, peak signal-to-noise ratio, distance reciprocal distortion, dan misclassification penalty metric. The average result showed that the proposed method achieved 88.18 and 89.04 of F-measure, for Jawi and HDIBCO-2016, respectively. The proposed method resulted in better performance compared with several benchmarking methods. It can be concluded that the proposed method succeeded to enhance binarization performance.</em></p><p class="Abstrak"><em><strong><br /></strong></em></p>

scholarly journals Unsupervised Neural Domain Adaptation for Document Image Binarization

2021 ◽  
pp. 108099
Author(s):  
Francisco J. Castellanos ◽  
Antonio-Javier Gallego ◽  
Jorge Calvo-Zaragoza
Keyword(s):  
Domain Adaptation ◽  
Document Image ◽  
Image Binarization ◽  
Document Image Binarization
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