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All Journal Jurnal Mandiri IT
Zulkarnaen Hatala
Politeknik Negeri Ambon, Indonesia
Computer Science & IT Library & Information Science Mathematics

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Handwritten text segmentation using deep learning method Zulkarnaen Hatala; Ahmad Thariq; Josseano Parera; Muhammad Hatala
Jurnal Mandiri IT Vol. 14 No. 4 (2026): April: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i4.509

Abstract

The rapid development of artificial intelligence and deep learning technologies has increased the risk of digital task fabrication in academic environments, encouraging educators to reintroduce handwritten assignments as an authentic evaluation method. In handwritten document analysis systems, background segmentation is a critical preprocessing step that separates text from complex document backgrounds. This study proposes the use of the U-Net deep learning architecture for background segmentation of handwritten document images. Two datasets were employed: the public cBAD dataset and a custom dataset consisting of Indonesian handwritten student assignments. Both datasets were processed using an identical pipeline and evaluated using 5-fold cross-validation. Model performance was measured using the Dice Similarity Coefficient and Intersection over Union (IoU). Experimental results show that the proposed U-Net model achieved an average Dice coefficient (F1-Score) of 0.74 on the cBAD dataset and 0.83 on the student assignment dataset. These results indicate that the model performs consistently and demonstrates stable generalization across cross-validation folds. Therefore, the proposed approach is suitable as an initial segmentation stage in handwritten document recognition systems.
Indonesian handwritten word segmentation using a neural word detector Zulkarnaen Hatala
Jurnal Mandiri IT Vol. 15 No. 2 (2026): Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v15i2.574

Abstract

Word segmentation is an essential preprocessing step in offline handwritten text recognition because accurate word localization directly affects subsequent recognition performance. This study presents an Indonesian handwritten word segmentation approach using a neural word detector designed to localize individual words in handwritten student assignment documents. The dataset consists of real handwritten student assignments, with word-level ground truth annotations manually created using LabelMe in the form of rectangular bounding boxes. A pretrained neural word detector was trained for five epochs and evaluated using an Intersection over Union (IoU) threshold of 0.50. Predicted word bounding boxes were matched with ground-truth boxes using the Hungarian assignment algorithm, with the matching cost defined from the IoU between corresponding boxes. Evaluation was conducted on 120 paired files containing 9,267 ground-truth word boxes and 9,308 evaluated predicted boxes. The detector achieved a precision of 92.35%, recall of 92.76%, and F1-score of 92.55%, while the mean IoU of matched bounding boxes reached 86.27%. These results indicate that the neural word detector can effectively localize handwritten Indonesian words with high detection consistency and spatial overlap. The study demonstrates the feasibility of neural word detection as a practical approach for word-level segmentation of real Indonesian handwritten documents. The resulting segmentation can provide localized word regions for subsequent handwritten word recognition and document analysis applications.
Co-Authors Josseano Parera Muhammad Hatala Thariq, Ahmad