Improving OCR Accuracy for Kazakh Handwriting Recognition Using GAN Models

Yeleussinov, Arman; Amirgaliyev, Yedilkhan; Cherikbayeva, Lyailya

doi:10.3390/app13095677

Cited by 10 publications

(6 citation statements)

References 33 publications

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“…For example, text processing tasks such as syntematic analysis of texts, clustering, predicting the next word, creating a question-and-answer system. Also, such developments can be used for handwriting recognition to correct words with incorrectly recognized letters [30]. If the text error is found accurately and correctly, this is a prerequisite for obtaining good results in the subsequent stages of processing.…”

Section: Discussion Of the Results Of Research On The Development Of ...mentioning

confidence: 99%

Development of an augmented Damerau–Levenshtein method for correcting spelling errors in Kazakh texts

Mukazhanov,

Alibiyeva,

Yerimbetova

et al. 2023

EEJET

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The presented paper is devoted to the development of a method for identifying and correcting spelling errors in Kazakh texts. In this paper, the study object is methods for more accurate correction of spelling errors in Kazakh texts. The aim of the study is to develop an augmented version of the Damerau-Levenshtein method for correcting spelling errors in Kazakh language texts. Automatic detection and correction of spelling errors have become a default feature in modern text editors for working with text data, in text messaging applications such as chatbots, messengers, etc. However, although this task is well solved in geographically widespread languages, it has not been fully solved in languages with a small audience, such as the Kazakh language. The methods developed so far cannot correct all spelling errors found in Kazakh texts. Therefore, the development of a method with specific algorithms for spelling error correction in Kazakh texts is considered. As a result of the research work, algorithms for correcting errors found in Kazakh language texts were developed, and the developed algorithms were included in the Damerau-Levenshtein method. The experimental testing results of the augmented Damerau- Levenshtein method showed 97.2 % accuracy in correcting specific errors found only in Kazakh words and 92.8 % accuracy in correcting common errors from letter symbols. The standard Damerau-Levenshtein method testing results showed 76.4 % accuracy in correcting specific errors found only in Kazakh words. The results of the tests in correcting common errors from letter symbols with the standard Damerau-Levenshtein were approximately the same with the augmented Damerau-Levenshtein method, the accuracy is 92.2 %. The extent and conditions of practical application of the results are implemented by including them in text editors, messengers, e-mails and similar applications that work with text data.

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Section: Discussion Of the Results Of Research On The Development Of ...mentioning

confidence: 99%

Development of an augmented Damerau–Levenshtein method for correcting spelling errors in Kazakh texts

Mukazhanov,

Alibiyeva,

Yerimbetova

et al. 2023

EEJET

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“…Following closely, the CVL-Database is popular for its focus on historical handwritten documents, while CASIA is valuable for research involving glyphbased language, making them the second most frequently employed, appearing in about 10.3% and 6.9% of the papers [27,32,34,35,37,40,49,50,53]. The third group of frequently used datasets includes IFN/ENIT and HKR, with 5.2% and 5.2% of the papers [35,41,43,56,57]. Finally, the least used datasets reached less than 5% of the papers.…”

Section: Discussionmentioning

confidence: 99%

“…The "Handwritten Kazakh and Russian" (HKR) dataset [72], representing Kazakh and Russian languages, has been utilized in the studies by [35,41,43]. Furthermore, the Chinese language is also represented by the dataset from the "Chinese Academy of Sciences' Institute of Automation" (CASIA) [73], that was used in the works of [34,37,50,53].…”

Section: Recognition Tasks and Datasetsmentioning

confidence: 99%

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Data Augmentation for Offline Handwritten Text Recognition: A Systematic Literature Review

de Sousa Neto,

Bezerra,

de Moura

et al. 2024

SN COMPUT. SCI.

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Offline Handwritten Text Recognition (HTR) systems concern the automatic recognition and transcription of handwritten text from scanned images to digital media. Recently, HTR research field has become increasingly important due to the growing need for digitizing documents and automating data entry across various industries. However, achieving satisfactory results depend on the amount of available samples to train an optical model. Creating and labeling large enough datasets for this purpose often require significant time and effort, that in some situations may be impractical. To address this problem, data augmentation approaches are commonly used as an essential component of HTR systems. In this way, the present work aims to identify, explore, and analyze the scope of data augmentation approaches for offline HTR systems. Furthermore, we detailed our research protocol and answered four pertinent research questions, which enabled us to discuss trends and possible gaps. A search was conducted across five scientific databases, focusing on papers published between 2012 and 2023. The search yielded 976 primary papers, with 32 meeting the criteria for inclusion in this review. Our results indicate that handwriting synthesis is an emerging research field, and we observed that Digital Image Processing (DIP) is still widely used as an image generator. Nevertheless, the application of Generative Adversarial Networks (GAN) has gained traction in recent years owing to its impressive ability to synthesize images of handwritten text with arbitrary style and content. In addition, we explored and analyzed the most commonly used datasets and text recognition levels in the selected works.

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“…Several notable applications of GANs have been discovered in existing studies. These applications include object detection [ 29 ], handwriting recognition [ 30 ], facial age progression [ 31 ] as a concept related to face recognition, super-resolution imaging [ 32 ], visual saliency prediction [ 33 ], and unsupervised domain adaptation [ 34 ]. GANs have been used by researchers for data augmentation due to their impressive performance in image synthesis.…”

Section: Related Studiesmentioning

confidence: 99%

A Comparative Analysis of the Novel Conditional Deep Convolutional Neural Network Model, Using Conditional Deep Convolutional Generative Adversarial Network-Generated Synthetic and Augmented Brain Tumor Datasets for Image Classification

Onakpojeruo,

Mustapha,

Ozsahin

et al. 2024

Brain Sciences

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Disease prediction is greatly challenged by the scarcity of datasets and privacy concerns associated with real medical data. An approach that stands out to circumvent this hurdle is the use of synthetic data generated using Generative Adversarial Networks (GANs). GANs can increase data volume while generating synthetic datasets that have no direct link to personal information. This study pioneers the use of GANs to create synthetic datasets and datasets augmented using traditional augmentation techniques for our binary classification task. The primary aim of this research was to evaluate the performance of our novel Conditional Deep Convolutional Neural Network (C-DCNN) model in classifying brain tumors by leveraging these augmented and synthetic datasets. We utilized advanced GAN models, including Conditional Deep Convolutional Generative Adversarial Network (DCGAN), to produce synthetic data that retained essential characteristics of the original datasets while ensuring privacy protection. Our C-DCNN model was trained on both augmented and synthetic datasets, and its performance was benchmarked against state-of-the-art models such as ResNet50, VGG16, VGG19, and InceptionV3. The evaluation metrics demonstrated that our C-DCNN model achieved accuracy, precision, recall, and F1 scores of 99% on both synthetic and augmented images, outperforming the comparative models. The findings of this study highlight the potential of using GAN-generated synthetic data in enhancing the training of machine learning models for medical image classification, particularly in scenarios with limited data available. This approach not only improves model accuracy but also addresses privacy concerns, making it a viable solution for real-world clinical applications in disease prediction and diagnosis.

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Improving OCR Accuracy for Kazakh Handwriting Recognition Using GAN Models

Cited by 10 publications

References 33 publications

Development of an augmented Damerau–Levenshtein method for correcting spelling errors in Kazakh texts

Development of an augmented Damerau–Levenshtein method for correcting spelling errors in Kazakh texts

Data Augmentation for Offline Handwritten Text Recognition: A Systematic Literature Review

A Comparative Analysis of the Novel Conditional Deep Convolutional Neural Network Model, Using Conditional Deep Convolutional Generative Adversarial Network-Generated Synthetic and Augmented Brain Tumor Datasets for Image Classification

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