Adaptasi Profil Proyeksi Horisontal dengan Stripping Vertikal untuk Segmentasi Baris Teks Miring Arab Jawi

Ade Jamal, Iin Suryaningsih, Arif Supriyanto

Abstract


Abstract – Line segmentation in handwritten Arab Jawi scripts poses a significant challenge for automatic character recognition systems due to high variations in writing slant. The primary problem arises when using the standard Horizontal Projection Profile (HPP) method, where text skewness causes an overlap between peaks and valleys in the projection graph, leading to inaccurate or failed line separation. This research aims to develop a line segmentation solution for skewed Arab Jawi text by adapting the HPP method through a vertical stripping technique. The methodology involves dividing the document image into several narrow vertical columns or strips, followed by independent horizontal projection calculations for each strip. Local line separation points from each strip are then sequentially connected to form a dynamic separation path that follows the original inclination angle of the text. Research findings demonstrate that this approach successfully separates skewed text lines perfectly without cutting through characters, while also effectively managing variations in manuscript colour degradation and inconsistent line sizes. In conclusion, the modification of HPP with vertical stripping proves to be effective and computationally efficient as a pre-processing stage for ancient Jawi manuscripts. This method offers a balance between the simplicity of classical algorithms and the robustness required to handle handwriting complexity, making it highly potential for integration into the development of broader Optical Character Recognition systems to support the preservation of historical Southeast Asian manuscripts


Keywords


Arab Jawi, Handwritten text, Horizontal projection profile; Line segmentation, Vertical stripping.

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References


A. Antonacopoulos dan D. Karatzas, “Document Image Analysis for World War II Personal Records,” Proc. - First Int. Work. Doc. Image Anal. Libr. - DIAL 2004, no. January, pp. 336–341, 2004, doi: 10.1109/DIAL.2004.1263263.

I. M. G. Sunarya, M. W. Antara Kesiman, dan I. A. P. Purnami, “Segmentasi Citra Tulisan Tangan Aksara Bali Berbasis Proyeksi Vertikal Dan Horisontal,” J. Inform., vol. 9, no. 1, pp. 982–992, 2015, doi: 10.26555/jifo.v9i1.a2039.

A. R. Himamunanto dan A. R. Widiarti, “Javanese character image segmentation of document image of Hamong Tani,” Proc. Digit. 2013 - Fed. 19th Int’l VSMM, 10th Eurographics GCH, 2nd UNESCO Mem. World Conf. Plus Spec. Sess. fromCAA, Arqueol. 2.0 al., vol. 1, no. August, pp. 641–644, 2013, doi: 10.1109/DigitalHeritage.2013.6743807.

M. R. Fauzi, N. Agus, dan A. Ajulian, “Mengubah Tulisan Tangan Menjadi Text Digital Ocr ( Optical Character Recognition ) Dengan Menggunakan Metode Segmentasi Dan Korelasi,” Transient,Sistem, pp. 1–5, 2014.

H. Masrani, Ilhamsyah, dan I. Ruslianto “Aplikasi Pengenalan Pola Pada Huruf Tulisan Tangan Menggunakan Jaringan Saraf Tiruan Dengan Metode Ekstraksi Fitur Geometri,” Coding J. Komput. dan Apl., vol. 6, no. 2, pp. 69–78, 2018, doi: 10.26418/coding.v6i2.26674.

A. R. Widiarti, A. Harjoko, M., dan S. Hartati, “Preprocessing Model of Manuscripts in Javanese Characters,” J. Signal Inf. Process., vol. 05, no. 04, pp. 112–122, 2014, doi: 10.4236/jsip.2014.54014.

R. Ptak, B. Zygadło, dan O. Unold, “Projection-based text line segmentation with a variable threshold,” Int. J. Appl. Math. Comput. Sci., vol. 27, no. 1, pp. 195–206, 2017, doi: 10.1515/amcs-2017-0014.

A. W. Mahastama dan L. D. Krisnawati, “Improving Projection Profile for Segmenting Characters from Javanese Manuscripts,” no. Creativearts 2019, pp. 77–82, 2020, doi: 10.5220/0008526900770082.

A. Yusuf, “Pengenalan Catatan Penjualan Menggunakan Pengenalan Angka Berbasis Korelasi,” Systemic, vol. 1, no. 1, pp. 14–19, 2015.

N. Ouwayed dan A. Belaïd, “A general approach for multi-oriented text line extraction of handwritten documents,” Int. J. Doc. Anal. Recognit., vol. 15, no. 4, pp. 297–314, 2012, doi: 10.1007/s10032-011-0172-6.

M. Farid, J. Santoso, dan E. Setyati, “Handwritten Image Segmentation Carakan Madura Based Projection And Connected Component Labeling,” JOINCS (Journal Informatics, Network, Comput. Sci., vol. 3, no. 2, pp. 33–37, 2020, doi: 10.21070/joincs.v3i0.823.

A. N. Putri dan I. P. G. H. Suputra, “Hijaiyah Letter Segmentation Using Connected Component Labeling Method,” JELIKU (Jurnal Elektron. Ilmu Komput. Udayana), vol. 9, no. 2, p. 249, 2020, doi: 10.24843/jlk.2020.v09.i02.p12.

Y. Sugianela dan N. Suciati, “Character Image Segmentation of Javanese Script Using Connected Component Method,” J. Ilmu Komput. dan Inf., vol. 12, no. 2, pp. 67–74, 2019, doi: 10.21609/jiki.v12i2.677.

M. Das dan M. Panda, “Seam carving, horizontal projection profile and contour tracing for line and word segmentation of language independent handwritten documents,” Results Eng., vol. 18, no. April, p. 101110, 2023, doi: 10.1016/j.rineng.2023.101110.

S. D. Alhafiz, Purnamasari, Y. N. Kunang, I. Z. Yadi, dan I. Effendy, “Segmentasi Citra Formulir Menggunakan Bounding box untuk Pengambilan Objek Gambar,” J. Pendidik. dan Teknol. Indones., vol. 5, no. 9, pp. 2900–2909, 2025.

Sakshi, C. Sharma, V. Bhardwaj dan G. Aggarwal, “Bridging classical and neural methods for improved segmentation in mathematical text based images IN IN,” Sci. Reports Artic. Press, 2025.

B. S. Wijaya, R. Munir, dan N. P. Utama, “Segmentasi Awan Pada Citra Satelit Multispektral Menggunakan Convolutional Neural Network,” J. Teknol. Inf. dan Ilmu Komput., vol. 12, no. 6, pp. 1251–1260, 2025.

A. Plaksyvyi, M. Skublewska-Paszkowska, dan P. Powroznik, “A Comparative Analysis of Image Segmentation Using Classical and Deep Learning Approach,” Adv. Sci. Technol. Res. J., vol. 17, no. 6, pp. 127–139, 2023, doi: 10.12913/22998624/172771.

D. Park, N. R. Yarram, S. Kim, M. Kim, S. Cho, dan T. Lee, “Text Change Detection in Multilingual Documents Using Image Comparison,” Proc. - 2025 IEEE Winter Conf. Appl. Comput. Vision, WACV 2025, pp. 5218–5227, 2025, doi: 10.1109/WACV61041.2025.00510.




DOI: https://doi.org/10.30591/jpit.v11i2.10085

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