Multiobjective optimization for recognition of isolated handwritten Indic scripts

Gupta, Anisha; Sarkhel, Ritesh; Das, Nibaran; Kundu, Mahantapas

doi:10.1016/j.patrec.2019.09.019

Cited by 31 publications

(10 citation statements)

References 19 publications

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“…In [25], the concept of the water reservoir is proposed for character segmentation. Features such as structural, contour, topological, templates [18], histogram of gradients [28,26,27], water reservoirs [20,25,29], writing strokes [22,30] are the most frequently used features for character recognition. A different approach is proposed in [31] where a stroke feature of segmented character and the whole word is derived and results are compared.…”

Section: Review Of Devanagari Document Text Recognition Methodsmentioning

confidence: 99%

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Recognition of Devanagari Scene Text Using Autoencoder CNN

Shiravale

Jayadevan²,

Sannakki³

2021

ELCVIA

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Scene text recognition is a well-rooted research domain covering a diverse application area. Recognition of scene text is challenging due to the complex nature of scene images. Various structural characteristics of the script also influence the recognition process. Text and background segmentation is a mandatory step in the scene text recognition process. A text recognition system produces the most accurate results if the structural and contextual information is preserved by the segmentation technique. Therefore, an attempt is made here to develop a robust foreground/background segmentation(separation) technique that produces the highest recognition results. A ground-truth dataset containing Devanagari scene text images is prepared for the experimentation. An encoder-decoder convolutional neural network model is used for text/background segmentation. The model is trained with Devanagari scene text images for pixel-wise classification of text and background. The segmented text is then recognized using an existing OCR engine (Tesseract). The word and character-level recognition rates are computed and compared with other existing segmentation techniques to establish the effectiveness of the proposed technique.

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Section: Review Of Devanagari Document Text Recognition Methodsmentioning

confidence: 99%

“…existing OCR engine is used for the recognition purpose. Most of the OCR engines are intended for document character reading [28]. Wider usage and success in printed and handwritten character recognition transformed an OCR system into a commercially available product.…”

Section: Recognition By Ocr Enginementioning

confidence: 99%

Recognition of Devanagari Scene Text Using Autoencoder CNN

Shiravale

Jayadevan²,

Sannakki³

2021

ELCVIA

View full text Add to dashboard Cite

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“…This method was verified with an independent Bengali dataset; it not only improved the identification accuracy but also lowered the computational cost [31,32]. Gupta et al proposed an improved multi-objective harmony search algorithm based on the handwritten character image information from a large set of handwritten Bengali and Sanskrit characters with very good results, reducing the identification cost, reducing the characteristics of redundancy, and improving the recognition rate [33]. Sarkhel et al proposed a feature extraction method based on a multiscale quadtree, which achieved good recognition of Indian characters and reduced the calculation cost [34].…”

Section: Related Researchmentioning

confidence: 97%

Offline handwritten Tai Le character recognition using ensemble deep learning

et al. 2021

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Handwriting recognition is an important area in pattern recognition. For many years, Tai Le has been widely used in Southwest China and Southeast Asia, which makes it of great interest for recognition research. The characteristics of the highly similar characters in Tai Le, such as its large proportion of similar characters and the randomness of its writing, bring great challenges to the task of recognition. In this paper, a method based on ensemble deep learning for offline handwritten Tai Le characters is proposed. First, the handwritten Tai Le character dataset SDH2019.2 was constructed and preprocessed. Then, an ensemble deep convolutional neural network (EDCNN) model was constructed by using a stacking strategy. Thirty deep neural network (DNN) and logistic regression algorithms were integrated into a strong Tai Le classifier by stacking. Experiments showed that the proposed model is competitive with the base DNN model and other ensemble models. The results indicate that the performance of Tai Le recognition by the stacking ensemble-based deep neural network model is high, with an accuracy of 98.85%. Additionally, its precision, recall and F1-score of 98.87%, 98.85% and 98.85%, respectively, are superior to those of other classic neural network models. To verify the general applicability of EDCNN, its effectiveness was also verified by recognizing MNIST handwritten digits and Devanagari handwritten characters.

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“…Recently, Gupta et al in [ 48 ] proposed a novel multi-objective optimization framework for identifying the most informative local regions from a character image. The work was also evaluated on isolated handwritten English numerals, namely, MNIST images, along with three other popular Indic scripts, namely, handwritten Bangala numerals and handwritten Devanagari characters.…”

Section: Related Workmentioning

confidence: 99%

Improved Handwritten Digit Recognition Using Convolutional Neural Networks (CNN)

Ahlawat¹,

Choudhary²,

Nayyar

et al. 2020

Sensors

201

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Traditional systems of handwriting recognition have relied on handcrafted features and a large amount of prior knowledge. Training an Optical character recognition (OCR) system based on these prerequisites is a challenging task. Research in the handwriting recognition field is focused around deep learning techniques and has achieved breakthrough performance in the last few years. Still, the rapid growth in the amount of handwritten data and the availability of massive processing power demands improvement in recognition accuracy and deserves further investigation. Convolutional neural networks (CNNs) are very effective in perceiving the structure of handwritten characters/words in ways that help in automatic extraction of distinct features and make CNN the most suitable approach for solving handwriting recognition problems. Our aim in the proposed work is to explore the various design options like number of layers, stride size, receptive field, kernel size, padding and dilution for CNN-based handwritten digit recognition. In addition, we aim to evaluate various SGD optimization algorithms in improving the performance of handwritten digit recognition. A network’s recognition accuracy increases by incorporating ensemble architecture. Here, our objective is to achieve comparable accuracy by using a pure CNN architecture without ensemble architecture, as ensemble architectures introduce increased computational cost and high testing complexity. Thus, a CNN architecture is proposed in order to achieve accuracy even better than that of ensemble architectures, along with reduced operational complexity and cost. Moreover, we also present an appropriate combination of learning parameters in designing a CNN that leads us to reach a new absolute record in classifying MNIST handwritten digits. We carried out extensive experiments and achieved a recognition accuracy of 99.87% for a MNIST dataset.

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Multiobjective optimization for recognition of isolated handwritten Indic scripts

Cited by 31 publications

References 19 publications

Recognition of Devanagari Scene Text Using Autoencoder CNN

Recognition of Devanagari Scene Text Using Autoencoder CNN

Offline handwritten Tai Le character recognition using ensemble deep learning

Improved Handwritten Digit Recognition Using Convolutional Neural Networks (CNN)

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