Handwritten Urdu character recognition via images using different machine learning and deep learning techniques

Chhajro, M. Ameen

doi:10.17485/ijst/v13i17.113

Cited by 11 publications

(4 citation statements)

References 16 publications

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“…UHaT [5] Dataset used in [18] Method Applied 5-Way-5-Shot 5-Way-1-Shot 5-Way-5-Shot 5-Way-1-Shot Matching Networks [45] 87.77 ±0.8% replicating the results obtained with very large set of training data used in conventional approaches [9], [10], [19], [34].…”

Section: Network/datasetmentioning

confidence: 87%

“…This is the author's version which has not been fully edited and content may change prior to final publication. 4 Method Applied % Accuracy (Characters) % accuracy (Numerals) CNN (pixel and geometrical based) [19] 96.04 98.3 Random Forest [10] 97 -Support Vector Machines (SVM) [10] 97 -Daubechies Wavelet [9] -92.05 Fuzzy Rule [34] -97.09 HMM [34] -97.45 Prototypical Networks (5-way-5-shot) 94.27 ±0.2% 88.31 ±0.3%…”

Section: B Future Workmentioning

confidence: 99%

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An Enhanced Prototypical Network Architecture for Few-Shot Handwritten Urdu Character Recognition

Sahay

Coustaty

2023

IEEE Access

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Few shot models have started to gain a lot of popularity in the past few years. This is mostly because these models grant the ability to structure the representation space (classes) using a very less amount of examples for each class. Such models are usually trained on a wide range of different classes and their examples, which allows them to form and learn a decision-based metric in the process. Non-Latin languages, especially languages such as Urdu, have a bi-linear direction of writing and are context-sensitive in nature, and are hard to recognize. Also, unlike traditional English, there is a very small amount of clean, collated, and usable data that is available for the Urdu language. In this paper, we explore a prototypical network for k-shot classification on handwritten Urdu characters. The prototypical network learns the Euclidean embeddings of the provided images and uses clusters to classify newer examples. Our improved method is able to outperform other methods of few-shot learning and is able to accurately classify both Urdu characters as well as numerals using a minimal number of examples. After comprehensive qualitative and quantitative evaluation and comparison of our proposed approach with other methods to classify handwritten text in few-shot settings, we found out that our proposed approach was typically able to beat other methods by a margin of 1% − 2% while relying on a small training set.

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Section: Network/datasetmentioning

confidence: 87%

Section: B Future Workmentioning

confidence: 99%

An Enhanced Prototypical Network Architecture for Few-Shot Handwritten Urdu Character Recognition

Sahay

Coustaty

2023

IEEE Access

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“…Husnain et al [13][14][15][16][17] contributed their work on the identifcation of Odia alphabets and digits. Researchers used neural networks and other deep learning approaches to contribute to the feld of character classifcation, as documented in [13,[18][19][20]. In [21], the authors contributed to work on image augmentation based on generative adversarial networks (GANs) on an ISI Kolkata handwritten dataset of Latin, Bangla, Devanagari, and Oriya languages.…”

Section: Related Work On Handwritten Character Recognitionmentioning

confidence: 99%

Enhancing the Power of CNN Using Data Augmentation Techniques for Odia Handwritten Character Recognition

Das

Panda

Dash³

2022

Advances in Multimedia

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The performance of any machine learning model largely depends on the type of input data provided. The higher the volume and variety of the data, the better the machine learning models get trained, thereby producing more accurate results. However, it is a challenging task to get high volume of data in some cases containing enough variety. Handwritten character recognition for Odia language is one of them. NITROHCS v1.0 for handwritten Odia characters and the ISI image database for handwritten Odia numerals are the standard Odia language datasets available for the research community. This paper shows the performance of five different machine learning models that uses a convolutional neural network to identify handwritten characters in response to handwritten datasets that are manipulated and expanded using several augmentation techniques to create variation and increase the volume of the data in the given dataset. These models, with the augmentation techniques discussed in the paper, even lead to a further increase in accuracy by approximately 1% across the models. The claims are supported by the results from the experiments done on the proposed convolutional neural network models on standard available Odia character and numeral data set.

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“…The main reason for excellent performance of Random forest is because it uses ensemble techniques which are able to remove the overfitting to a great extent. It was proposed by Breiman [27] and since then several researchers [28][29][30][31] have reported its superior performance for handwriting classification.…”

Section: Random Forest Classifiermentioning

confidence: 99%

Handwritten Gurmukhi Digit Recognition System for Small Datasets

Singh¹,

Agrawal²,

Sohi³

2020

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In the present study, a method to increase the recognition accuracy of Gurmukhi (Indian Regional Script) Handwritten Digits has been proposed. The proposed methodology uses a DCNN (Deep Convolutional Neural Network) with a cascaded XGBoost (Extreme Gradient Boosting) algorithm. Also, a comprehensive analysis has been done to apprehend the impact of kernel size of DCNN on recognition accuracy. The reason for using DCNN is its impressive performance in terms of recognition accuracy of handwritten digits, but in order to achieve good recognition accuracy, DCNN requires a huge amount of data and also significant training/testing time. In order to increase the accuracy of DCNN for a small dataset more images have been generated by applying a shear transformation (A transformation that preserves parallelism but not length and angles) to the original images. To address the issue of large training time only two hidden layers along with selective cascading XGBoost among the misclassified digits have been used. Also, the issue of overfitting is discussed in detail and has been reduced to a great extent. Finally, the results are compared with performance of some recent techniques like SVM (Support Vector Machine) Random Forest, and XGBoost classifiers on DCT (Discrete Cosine Transform) and DWT (Discrete Wavelet Transform) features obtained on the same dataset. It is found that proposed methodology can outperform other techniques in terms of overall rate of recognition.

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Handwritten Urdu character recognition via images using different machine learning and deep learning techniques

Cited by 11 publications

References 16 publications

An Enhanced Prototypical Network Architecture for Few-Shot Handwritten Urdu Character Recognition

An Enhanced Prototypical Network Architecture for Few-Shot Handwritten Urdu Character Recognition

Enhancing the Power of CNN Using Data Augmentation Techniques for Odia Handwritten Character Recognition

Handwritten Gurmukhi Digit Recognition System for Small Datasets

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