Convolutional Neural Networks for the Recognition of Malayalam Characters

Anil, Rohan; Manjusha, K.; Kumar, Sachin; Soman, K. P.

doi:10.1007/978-3-319-12012-6_54

Cited by 25 publications

(9 citation statements)

References 8 publications

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“…This study differs from other existed work because it reveals the effectiveness of CNN in terms of high accuracy and low computational time to classify handwritten digits [36][37][38][39]. With extensive literature survey, it comes to know that accuracy of various reported CNN models is not up to the mark as mentioned in Table 3.…”

Section: Introductionmentioning

confidence: 70%

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An efficient and improved scheme for handwritten digit recognition based on convolutional neural network

et al. 2019

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Character recognition from handwritten images has received greater attention in research community of pattern recognition due to vast applications and ambiguity in learning methods. Primarily, two steps including character recognition and feature extraction are required based on some classification algorithm for handwritten digit recognition. Former schemes exhibit lack of high accuracy and low computational speed for handwritten digit recognition process. The aim of the proposed endeavor was to make the path toward digitalization clearer by providing high accuracy and faster computational for recognizing the handwritten digits. The present research employed convolutional neural network as classifier, MNIST as dataset with suitable parameters for training and testing and DL4J framework for hand written digit recognition. The aforementioned system successfully imparts accuracy up to 99.21% which is higher than formerly proposed schemes. In addition, the proposed system reduces computational time significantly for training and testing due to which algorithm becomes efficient.

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Section: Introductionmentioning

confidence: 70%

“…According to work, the accuracy of classified digits for CNN is > 98% with some error rates. Anil et al [38] presented CNN trained with gradient-based learning and backpropagation algorithm for the recognition of Malayalam characters. Their algorithm produced a maximum of 75% accuracy.…”

Section: Introductionmentioning

confidence: 99%

An efficient and improved scheme for handwritten digit recognition based on convolutional neural network

et al. 2019

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“…When a training set is large, stochastic gradient descent is often preferred over batch gradient descent as batch gradient descent tries to scan through the entire training set before taking a single step which is a costly operation if the training sample (n) is very large [26], [27]. Models often involve some unknown parameters.…”

Section: B Supervised Learningmentioning

confidence: 99%

A New Hybrid Convolutional Neural Network and eXtreme Gradient Boosting Classifier for Recognizing Handwritten Ethiopian Characters

et al. 2020

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Handwritten character recognition has been profoundly studied for many years in the field of pattern recognition. Due to its vast practical applications and financial implications, the handwritten character recognition is still an important research area. In this research, a Handwritten Ethiopian Character Recognition (HECR) dataset is prepared to train a model. Images in the HECR dataset were organized with more than one color pen RGB main spaces that are size normalized to 28 × 28 pixels. The dataset is a combination of scripts (Fidel in Ethiopia), numerical representations, punctuations, tonal symbols, combining symbols, and special characters. These scripts have been used to write ancient histories, science, and arts of Ethiopia and Eritrea. In this study, a hybrid model of two super classifiers: Convolutional Neural Network (CNN), as well as eXtreme Gradient Boosting (XGBoost), are proposed for classification. In this integrated model, CNN works as a trainable automatic feature extractor from the raw images and XGBoost takes the extracted features as an input for recognition and classification. The output error rates of the hybrid model and CNN with a fully connected layer are compared. A 0.4630 and 0.1612 error rates were achieved in classifying the handwritten testing dataset images, respectively. The XGBoost as a classifier gave better results than the traditional fully connected layer.

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“…A handwritten Hangul character recognition system using deep convolutional neural network by proposing several novel techniques to increase the performance and training speed of the networks is done by [18]. In Malayalam handwritten character recognition using the convolutional neural network is developed and in their work they discussed the CNN is better than the conventional handcrafted feature extractor based systems [19]. Deep learning based large scale handwritten Devanagari character recognition is proposed by with focus on the use of dropout and dataset increment approach to increase the test accuracy of their deep learning model [20].…”

Section: Figure 1 Part Of the Amharic Alphabetmentioning

confidence: 99%

Handwritten Amharic Character Recognition System Using Convolutional Neural Networks

Abdurahman¹

2019

NWSA

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Amharic language is an official language of the federal government of the Federal Democratic Republic of Ethiopia. Accordingly, there is a bulk of handwritten Amharic documents available in libraries, information centres, museums, and offices. Digitization of these documents enables to harness already available language technologies to local information needs and developments. Converting these documents will have a lot of advantages including (i) to preserve and transfer history of the country (ii) to save storage space (ii) proper handling of documents (iv) enhance retrieval of information through internet and other applications. Handwritten Amharic character recognition system becomes a challenging task due to inconsistency of a writer, variability in writing styles of different writers, relatively large number of characters of the script, high interclass similarity, structural complexity and degradation of documents due to different reasons. In order to recognize handwritten Amharic character a novel method based on deep neural networks is used which has recently shown exceptional performance in various pattern recognition and machine learning applications, but has not been endeavoured for Ethiopic script. The Convolutional neural network model is evaluated for its performance using our database that contains 132,500 datasets of handwritten Amharic characters. Common handwritten recognition systems using machine learning use a combination of both feature extractors and classifiers. Currently the use of deep learning techniques shows promising improvements for machine learning based classification tasks. Our proposed CNN model gives an accuracy of 91.83% on training data and 90.47% on validation data.

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Convolutional Neural Networks for the Recognition of Malayalam Characters

Cited by 25 publications

References 8 publications

An efficient and improved scheme for handwritten digit recognition based on convolutional neural network

An efficient and improved scheme for handwritten digit recognition based on convolutional neural network

A New Hybrid Convolutional Neural Network and eXtreme Gradient Boosting Classifier for Recognizing Handwritten Ethiopian Characters

Handwritten Amharic Character Recognition System Using Convolutional Neural Networks

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