Malayalam Handwritten Character Recognition Using AlexNet Based Architecture

James, Ajay; Manjusha, J; Chandran, Saravanan

doi:10.11591/ijeei.v6i4.518

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…Several studies were succussed in utilizing the previously learned parameters of AlexNet for extracting features in numerous HCR tasks [29,30]. It is observed that every time the training set increases, the AlexNet recognition rate also increases.…”

Section: Alexnet Fine-tuningmentioning

confidence: 99%

A Novel Siamese Network for Few/Zero-Shot Handwritten Character Recognition Tasks

Elaraby¹,

Barakat²,

Rezk³

2023

Computers, Materials &Amp; Continua

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Deep metric learning is one of the recommended methods for the challenge of supporting few/zero-shot learning by deep networks. It depends on building a Siamese architecture of two homogeneous Convolutional Neural Networks (CNNs) for learning a distance function that can map input data from the input space to the feature space. Instead of determining the class of each sample, the Siamese architecture deals with the existence of a few training samples by deciding if the samples share the same class identity or not. The traditional structure for the Siamese architecture was built by forming two CNNs from scratch with randomly initialized weights and trained by binary cross-entropy loss. Building two CNNs from scratch is a trial and error and time-consuming phase. In addition, training with binary crossentropy loss sometimes leads to poor margins. In this paper, a novel Siamese network is proposed and applied to few/zero-shot Handwritten Character Recognition (HCR) tasks. The novelties of the proposed network are in. 1) Utilizing transfer learning and using the pre-trained AlexNet as a feature extractor in the Siamese architecture. Fine-tuning a pre-trained network is typically faster and easier than building from scratch. 2) Training the Siamese architecture with contrastive loss instead of the binary cross-entropy. Contrastive loss helps the network to learn a nonlinear mapping function that enables it to map the extracted features in the vector space with an optimal way. The proposed network is evaluated on the challenging Chars74K datasets by conducting two experiments. One is for testing the proposed network in few-shot learning while the other is for testing it in zero-shot learning. The recognition accuracy of the proposed network reaches to 85.6% and 82% in few-and zero-shot learning respectively. In addition, a comparison between the performance of the proposed Siamese network and the traditional Siamese CNNs is conducted. The comparison results show that the proposed network achieves higher recognition results in less time. The proposed network reduces the training time from days to hours in both experiments.

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Section: Alexnet Fine-tuningmentioning

confidence: 99%

A Novel Siamese Network for Few/Zero-Shot Handwritten Character Recognition Tasks

Elaraby¹,

Barakat²,

Rezk³

2023

Computers, Materials &Amp; Continua

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“…A character-level text ConvNets is presented in [8] for English and Chinese corpus by transfer learning to endeavor and reuse the important portrayals that are found out in the ConvNets from an enormous scope dataset. AlexNet the most robust CNN which is pre-trained with the ImageNet dataset has been exploited for character recognition of many scripts like Devanagari [9], Malayalam [10], Korean [11], and Tamil [12]. Manually written Devanagari character acknowledgment has been introduced in [13] using layer-wise preparation of Deep CNN and accomplished great results using six different adaptive gradient methods.…”

Section: Introductionmentioning

confidence: 99%

Multi-Domain Deep Convolutional Neural Network for Ancient Urdu Text Recognition System

Aarif¹,

Sivakumar²

2022

Intelligent Automation &Amp; Soft Computing

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Deep learning has achieved magnificent success in the field of pattern recognition. In recent years Urdu character recognition system has significantly benefited from the effectiveness of the deep convolutional neural network. Majority of the research on Urdu text recognition are concentrated on formal handwritten and printed Urdu text document. In this paper, we experimented the Challenging issue of text recognition in Urdu ancient literature documents. Due to its cursiveness, complex word formation (ligatures), and context-sensitivity, and inadequate benchmark dataset, recognition of Urdu text from the literature document is very difficult to process compared to the formal Urdu text document. In this work, first, we generated a dataset by extracting the recurrent ligatures from an ancient Urdu fatawa book. Secondly, we categorized and augment the ligatures to generate batches of augmented images that improvise the training efficiency and classification accuracy. Finally, we proposed a multi-domain deep Convolutional Neural Network which integrates a spatial domain and a frequency domain CNN to learn the modular relations between features originating from the two different domain networks to train and improvise the classification accuracy. The experimental results show that the proposed network with the augmented dataset achieves an averaged accuracy of 97.8% which outperforms the other CNN models in this class. The experimental results also show that for the recognition of ancient Urdu literature, well-known benchmark datasets are not appropriate which is also verified with our prepared dataset.

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