Recognition of Brahmi Words by Using Deep Convolutional Neural Network

Gautam, Neha; Chai, Soo See; Jose, Jais

doi:10.20944/preprints202005.0455.v1

Cited by 6 publications

(5 citation statements)

References 32 publications

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“…Existing Brahmi word recognition systems have performed well; however, their performance can also be increased. In the recently, Gautam, et al [12] used DCNN to recognize the Brahmi words and achieved 92.47% accuracy, which is very good.…”

Section: Literature Reviewmentioning

confidence: 99%

Brahmi Word Recognition by Supervised Techniques

Gautam¹,

Chai²,

Afrin³

et al. 2020

Preprint

Self Cite

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Significant progress has made in pattern recognition technology. However, one obstacle that has not yet overcome is the recognition of words in the Brahmi script, specifically the recognition of characters, compound characters, and word because of complex structure. For this kind of complex pattern recognition problem, it is always difficult to decide which feature extraction and classifier would be the best choice. Moreover, it is also true that different feature extraction and classifiers offer complementary information about the patterns to be classified. Therefore, combining feature extraction and classifiers, in an intelligent way, can be beneficial compared to using any single feature extraction. This study proposed the combination of HOG +zonal density with SVM to recognize the Brahmi words. Keeping these facts in mind, in this paper, information provided by structural and statistical based features are combined using SVM classifier for script recognition (word-level) purpose from the Brahmi words images. Brahmi word dataset contains 6,475 and 536 images of Brahmi words of 170 classes for the training and testing, respectively, and the database is made freely available. The word samples from the mentioned database are classified based on the confidence scores provided by support vector machine (SVM) classifier while HOG and zonal density use to extract the features of Brahmi words. Maximum accuracy suggested by system is 95.17% which is better than previously suggested studies.

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Section: Literature Reviewmentioning

confidence: 99%

Brahmi Word Recognition by Supervised Techniques

Gautam¹,

Chai²,

Afrin³

et al. 2020

Preprint

Self Cite

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“…The author used around 4,000 samples for each class to train bangla characters. CNN with gabor filter 91.65% CNN with dropout and gabor filter 92.47% [10].…”

Section: Review Of Related Literaturementioning

confidence: 99%

Convolutions vs. Sequences: Understanding performances of neural-based methods for automatic Baybayin script recognition

Rosa¹,

Lagunilla²,

Ramos³

et al. 2023

Third International Conference on Computer Vision and Information Technology (CVIT 2022)

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Common approaches to vision-based tasks such as character and object recognition use Convolutional Neural Networks (CNNs) due to their practicality in processing images and theoretical grounding. In this work, we take a different perspective in the task of Baybayin script recognition by exploring Vision Transformers, a new paradigm for processing images inspired by the Transformer model. We compare performances of CNNs and ViT and analyzed model confidence on a set of test images using Local Interpretable Model-Agnostic Explanations (LIME). Results show that, performancewise, convolution-based architectures (CNNS) still outperform sequence-based methods (ViT) for discriminating Baybayin scripts with a nearly doubled performance of 84.5% to 48.8% in accuracy respectively.

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“…Further, the historical Kannada handwritten characters are recognised using the line segmentation approach with LBP features in [17], and the SVM classifier achieved a good performance, with an accuracy of 96.4%. In [18], a CNN was used with dropout to recognise Brahmi words, with a 92.47% accuracy. As well, [19] designed and developed an automatic recognition tool for variant characters to assess tablet inscriptions, leading to an accuracy of the trained model ResNet50-18 of 90%.…”

Section: Literature Reviewmentioning

confidence: 99%

Recognition and Translation of Ancient South Arabian Musnad Inscriptions

Altalhi,

Alwethinani,

Alghamdi

et al. 2023

IJACSA

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Inscriptions play an important role in preserving historical information. As such, conservation of these inscriptions provides valuable insights into the history and cultural heritage of the region. Musnad inscriptions are considered one of the earliest forms of writing from the Arabian Peninsula, preceding the modern Arabic font; however, most Musnad inscriptions remain unread and untranslated, signifying a substantial loss of historical information. In response, this paper represents a significant contribution to the field by proposing a successful approach to interpreting Musnad inscriptions. To do so, a dataset was prepared from the Saudi Arabian Ministry of Culture and subjected to preprocessing for optimal recognition, a step that entailed several experiments to enhance image quality and preparedness for recognition. The dataset was then trained and tested with 29 classes using three different convolutional neural network (CNN) architectures: Visual Geometry Group 16 (VGG16), Residual Network 50 (ResNet50) and MobileNetV2. Thereafter, the performance of each architecture was evaluated based on its accuracy in recognising Musnad inscriptions. The results demonstrate that VGG16 achieved the highest accuracy of 93.81%, followed by ResNet50 at 89.39% and MobileNetV2 at 80.02%.

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Recognition of Brahmi Words by Using Deep Convolutional Neural Network

Cited by 6 publications

References 32 publications

Brahmi Word Recognition by Supervised Techniques

Brahmi Word Recognition by Supervised Techniques

Convolutions vs. Sequences: Understanding performances of neural-based methods for automatic Baybayin script recognition

Recognition and Translation of Ancient South Arabian Musnad Inscriptions

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