A New Scheme for Text Line and Character Segmentation from Gray Scale Images of Palm Leaf Manuscript

Kesiman, Made Windu Antara; Burie, Jean-Christophe; Ogier, Jean-Marc

doi:10.1109/icfhr.2016.0068

Cited by 17 publications

(4 citation statements)

References 20 publications

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“…It achieved an F-score of 94.4% and a BLEU score of 26.95 for translation from ancient into contemporary language and a score of 36.34 BLEU when translating from contemporary into ancient. (Windu et al, 2016) focused on various tasks that demonstrated the application of deep learning techniques in Natural Language Processing, different hardware, software and popular corpora were used for this purpose. The multiple NLP classifications were used, such as sequence classification, pair wise classification and word labeling etc.…”

Section: Related Workmentioning

confidence: 99%

A Novel Framework for Ancient Text Translation Using Artificial Intelligence

Verma

Gupta

Anil³

et al. 2023

ADCAIJ

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Ancient script has been a repository of knowledge, culture and civilization history. In order to have a greater access to the valuable information present in the ancient scripts, an appropriate translation system needs to be developed keeping complexity and very less knowledge of the script available in consideration. In this study, a translation and prediction system has been implemented using Artificial Intelligence. The training has been developed using Sunda-Dataset and self-generated dataset, whereas the translation from ancient script viz. Sundanese script to English text is done using two layers Recurrent Neural Network. The technique used is compared with an existing translator called IM Translator. The results shows that the BLEU score is increased by 8% in comparison to IM Translator further WER is decreased by 10% in contrast to IM Translator. Furthermore, the N-Gram analysis results indicate 3% to 4% increase in 100% contrast value.

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Section: Related Workmentioning

confidence: 99%

A Novel Framework for Ancient Text Translation Using Artificial Intelligence

Verma

Gupta

Anil³

et al. 2023

ADCAIJ

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“…To our knowledge, no research has been carried out to detect Balinese script characters, especially on Lontar manuscripts. Several studies have been conducted in relation to detecting Balinese script [18], [20], [27], but none have been able to detect characters of Balinese script in Lontar manuscripts. Furthermore, previous researches have not detected Balinese scripts based on characters but rather based on isolated word images.…”

Section: Performance Evaluationmentioning

confidence: 99%

LONTAR_DETC: Dense and High Variance Balinese Character Detection Method in Lontar Manuscripts

2022

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This paper proposed LONTAR_DETC, a method to detect handwritten Balinese characters in Lontar manuscripts. LONTAR_DETC is a deep learning architecture based on YOLO. The detection of Balinese characters in Lontar manuscripts is challenging due to the characteristics of Balinese characters in Lontar manuscripts. Balinese characters in Lontar manuscripts are dense, overlapping, have high variance, contain noise, and classes of these characters are imbalanced. The proposed method consists of three steps, namely data generation, Lontar manuscript annotation, and Balinese character detection. The first step is data generation, in which synthetic images of original Lontar manuscript images are generated with enhanced image quality. The second step is data annotation to build a new Lontar manuscript dataset. As a result, we also propose the Handwritten Balinese Character of Lontar manuscript (HBCL_DETC) dataset, a novel Balinese character in Lontar manuscripts dataset. HBCL_DETC contains 600 images that consists of more than 100,000 Balinese characters annotated by experts. Finally, the third step is training the YOLOv4 detection model using the HBCL_DETC dataset. We created this dataset specifically for the task of detecting Balinese characters in Lontar manuscripts. To evaluate the reliability of the dataset, we experimented with three scenarios. In the first scenario, the detection model was trained using original images of Lontar manuscripts, in the second scenario the detection model was trained with the addition of augmented grayscale images, and in the third scenario the detection model was trained using HBCL_DETC. Based on the experimental results, LONTAR_DETC can detect Balinese characters with high detection rate with mAP of 99.55%.

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“…Another conventional approach of great concern is the projection algorithm. Kesiman et al [5] segmented characters by projecting images from two directions. Commonly, if the spacing of all characters is apparent, these methods are effectual and time-saving.…”

Section: Introductionmentioning

confidence: 99%

TSER: A Two-Stage Character Segmentation Network With Two-Stream Attention and Edge Refinement

et al. 2020

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Segmenting characters in an image is a classic yet challenging task in computer vision. Correctly determining boundaries of adhesive characters with various scales and shapes is essential for character segmentation, especially for separating handwritten characters. Nevertheless, there is seldom work in the literature which can achieve satisfactory performance. In this article, by leveraging the ability of deep neural networks, we proposed a two-stage character segmentation network with two-stream attention and edge refinement (TSER) to tackle this problem. TSER firstly locates every character by object detection, then extracts their corresponding contours. In the process, a novel two-stream attention mechanism (TSAM) is proposed to make the network focus more on the discrepancy of character boundaries. Furthermore, a novel generating method is used to dynamically generate anchors on different feature levels to improve model's sensitivity on the shapes and scales of characters. Eventually a cascaded edge refinement network is used to obtain contour of each character. To prove the efficiency and generalization ability of our model, we compared TSER with traditional algorithms and other deep learning models on two commonly used datasets in different segmentation tasks. The comparative result indicated that TSER reached state-of-the-art performance.

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A New Scheme for Text Line and Character Segmentation from Gray Scale Images of Palm Leaf Manuscript

Cited by 17 publications

References 20 publications

A Novel Framework for Ancient Text Translation Using Artificial Intelligence

A Novel Framework for Ancient Text Translation Using Artificial Intelligence

LONTAR_DETC: Dense and High Variance Balinese Character Detection Method in Lontar Manuscripts

TSER: A Two-Stage Character Segmentation Network With Two-Stream Attention and Edge Refinement

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