Multilingual Text &amp; Handwritten Digit Recognition and Conversion of Regional languages into Universal Language Using Neural Networks

Vidhale, Bhushan R.; Khekare, Ganesh; Dhule, Chetan; Chandankhede, Pankaj H.; Titarmare, Abhijit; Tayade, Meenal

doi:10.1109/i2ct51068.2021.9418106

Cited by 22 publications

(4 citation statements)

References 7 publications

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“…Additionally, it is the first attempt to develop a fusion-free approach for recognizing handwritten numerals in eight different languages, independent of the script used in each language, challenging the notion of multilingualism in handwritten character recognition. The research work [19] developed a knowledgeable framework for Handwritten Character Recognition (HCR) using Neural Networks, which can accurately identify specific type-format characters. The proposed methodology involves the utilization of both a machine learning model and a character recognition MATLAB model to recognize and identify handwritten digits accurately.…”

Section: E Multilingual Approachmentioning

confidence: 99%

“…For instance, Abhishek Sethy proposed a subsequential method that combined LS-SVM and RF classifiers, achieving an impressive overall accuracy of 99.01% on handwritten Odia character dataset [16]. In recent years, the advent of deep learning has led to a renewed focus on improving the effectiveness of systems for recognizing handwritten scripts, particularly through the use of RNNs (Recurrent Neural Networks) and CNNs (Convolutional Neural Networks) [2,19,20]. Notably, Abu Sufian introduced an end-to-end approach utilizing a densely connected convolutional neural network (BDNet) and achieved a remarkable accuracy of 99.78% on ISI Bengali handwritten numerals [21].…”

Section: Introductionmentioning

confidence: 99%

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Advancing Multilingual Handwritten Numeral Recognition With Attention-Driven Transfer Learning

Fateh,

Birgani,

Fateh

et al. 2024

IEEE Access

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As deep learning continues to evolve, we have observed huge breakthroughs in the fields of medical imaging, video and frame generation, optical character recognition (OCR), and other domains. In the field of data analysis and document processing, the recognition of handwritten numerals plays a crucial role. This work has led to remarkable changes in OCR, historical handwritten document analysis, and postal automation. In this study, we present a novel framework to overcome this challenge, going beyond digit recognition in only one language. Unlike common methods that focus on a limited set of languages, our method provides a comprehensive solution for recognition of handwritten digit images in 12 different languages. These specific languages are chosen because most of them have fairly distant representations in latent space. We utilize transfer learning, as it reduces the computational cost and maintains the quality of enhanced images and the models' recognition accuracy. Another strength of our approach is the innovative attention-based module called the MRA module. Our experiments confirm that by applying this module, major progress is made in both image quality and the accuracy of handwritten digit recognition. Notably, we reached high precisions, surpassing nearly 2% improvement in specific languages compared to earlier techniques. In this work, we present a robust and cost-effective approach that handles multilingual handwritten numeral recognition across a wide range of languages. The code and further implementation details are available at https://github.com/CVLab-SHUT/HandWrittenDigitRecognition

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Section: E Multilingual Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Advancing Multilingual Handwritten Numeral Recognition With Attention-Driven Transfer Learning

Fateh,

Birgani,

Fateh

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The research work [19] developed a knowledgeable framework for Handwritten Character Recognition (HCR) using Neural Networks, which can accurately identify speci c type-format characters. The proposed methodology involves the utilization of both a machine learning model and a character recognition MATLAB model to recognize and identify handwritten digits accurately.…”

Section: Multilingual Learning Approachmentioning

confidence: 99%

“…For instance, Abhisek Sethy proposed a subsequential method that combined LS-SVM and RF classi ers, achieving an impressive overall accuracy of 99.01% on handwritten Odia character datasets [16]. In recent years, the advent of deep learning has sparked renewed interest in enhancing the effectiveness of systems for recognizing handwritten scripts, particularly through the use of RNNs (Recurrent Neural Networks) and CNNs (Convolutional Neural Networks) [18][19][20]. Notably, Abu Su an introduced an end-to-end approach utilizing a densely connected convolutional neural network (BDNet) and achieved a remarkable accuracy of 99.78% on ISI Bengali handwritten numerals [21].…”

Section: Introductionmentioning

confidence: 99%

Unveiling Cross-Linguistic Mastery: Advancing Multilingual Handwritten Numeral Recognition with Attention-driven Transfer Learning

Fateh,

Birgani,

Fateh

2023

Preprint

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In the realm of data analysis and document processing, the recognition of handwritten numerals stands as a pivotal advancement. This contribution has steered transformative shifts in optical character recognition, historical handwritten document analysis, and postal automation. A persistent challenge in this arena is the recognition of handwritten digits across a spectrum of languages, each with its idiosyncrasies. We present an innovative paradigm to surmount this hurdle, transcending the confines of monolingual recognition. Unlike the status quo, which gravitates toward a narrow subset of languages, our method orchestrates a comprehensive solution spanning 12 distinct languages, deftly navigating linguistic intricacies. The catalyst for this efficacy is transfer learning, amplifying image quality and recognition acumen. Emboldening this framework is an ingenuity-charged attention-based module that refines precision. Our rigorous experimentations substantiate quantum leaps in image quality and the prowess of linguistic and numeral recognition. Notably, we unearth significant accuracy strides, eclipsing 2% enhancements in specific languages vis-à-vis antecedent methodologies. This endeavor epitomizes a sturdy, economically sound avenue, unshackling multilingual handwritten numeral recognition to an expansive spectrum of languages.

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Enhancing disaster detection on social media with natural language processing

Bagde,

Bhagat,

Meshram

et al. 2024

AIP Conference Proceedings

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Multilingual Text & Handwritten Digit Recognition and Conversion of Regional languages into Universal Language Using Neural Networks

Cited by 22 publications

References 7 publications

Advancing Multilingual Handwritten Numeral Recognition With Attention-Driven Transfer Learning

Advancing Multilingual Handwritten Numeral Recognition With Attention-Driven Transfer Learning

Unveiling Cross-Linguistic Mastery: Advancing Multilingual Handwritten Numeral Recognition with Attention-driven Transfer Learning

Enhancing disaster detection on social media with natural language processing

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