Recognition of Urdu Handwritten Characters Using Convolutional Neural Network

Husnain, Mujtaba; Missen, Malik Muhammad Saad; Mumtaz, Shahzad; Jhanidr, Muhammad Zeeshan; Coustaty, Mickaël; Luqman, Muhammad; Ogier, Jean-Marc; Choi, Gyu Sang

doi:10.3390/app9132758

Cited by 45 publications

(40 citation statements)

References 48 publications

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“…The input size of the dataset was 28 × 28. Our proposed model achieved an improved accuracy of 99%, which is 2.96% better than the implementation in [ 42 ]. The authors of this paper used geometrical feature extraction of each character in their dataset in a vector before feeding it into the CNN.…”

Section: Experimental Setup and Resultsmentioning

confidence: 81%

“…During experimentation, the dataset of [ 42 ] was fed to our model. This dataset contains 40 Urdu characters with 500 images for each character, that is, 500 × 40 = 20,000 character images.…”

Section: Experimental Setup and Resultsmentioning

confidence: 99%

“…This research work lacked in performance due to insufficient training data. In [ 42 ], a novel algorithm was proposed, in which complicated inputs are preprocessed and the shape of the actual input is maintained. The secondary stroke is linked to the primary stroke by fuzzy association rules.…”

Section: Literature Reviewmentioning

confidence: 99%

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Recognition of Pashto Handwritten Characters Based on Deep Learning

Amin

Yasir

Ahn

2020

Sensors

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Handwritten character recognition is increasingly important in a variety of automation fields, for example, authentication of bank signatures, identification of ZIP codes on letter addresses, and forensic evidence. Despite improved object recognition technologies, Pashto’s hand-written character recognition (PHCR) remains largely unsolved due to the presence of many enigmatic hand-written characters, enormously cursive Pashto characters, and lack of research attention. We propose a convolutional neural network (CNN) model for recognition of Pashto hand-written characters for the first time in an unrestricted environment. Firstly, a novel Pashto handwritten character data set, “Poha”, for 44 characters is constructed. For preprocessing, deep fusion image processing techniques and noise reduction for text optimization are applied. A CNN model optimized in the number of convolutional layers and their parameters outperformed common deep models in terms of accuracy. Moreover, a set of benchmark popular CNN models applied to Poha is evaluated and compared with the proposed model. The obtained experimental results show that the proposed model is superior to other models with test accuracy of 99.64 percent for PHCR. The results indicate that our model may be a strong candidate for handwritten character recognition and automated PHCR applications.

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Section: Experimental Setup and Resultsmentioning

confidence: 81%

Section: Experimental Setup and Resultsmentioning

confidence: 99%

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Recognition of Pashto Handwritten Characters Based on Deep Learning

Amin

Yasir

Ahn

2020

Sensors

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“…Figure 1 shows the shape of the handwritten Arabic characters according to their position. In [15], the authors proposed a new approach to recognize offline Urdu handwritten characters and numerals using CNNs. The authors extract geometrical features of the characters and numerals first in order to embed them with pixel-based data that will be extracted by CNN.…”

Section: Related Workmentioning

confidence: 99%

Towards Directing Convolutional Neural Networks Using Computational Geometry Algorithms: Application to Handwritten Arabic Character Recognition

Elkhayati¹,

Elkettani²

2020

Adv. sci. technol. eng. syst. j.

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Suppose we want to classify a query item Q with a classification model that consists of a large set of predefined classes L and suppose we have a knowledge that indicates to us that the target class of Q belongs to a small subset from L. Naturally, this filtering will improve the accuracy of any classifier, even random guessing. Based on this principle, this paper proposes a new classification approach using convolutional neural networks (CNN) and computational geometry (CG) algorithms. The approach is applied and tested on the recognition of isolated handwritten Arabic characters (IHAC). The main idea of the proposed approach is to direct CNN using a filtering layer, which reduces the set of possible classes for a query item. The rules of the relative neighborhood graph (RNG) and Gabriel's graph (GG) are combined for this purpose. The choice of RNG-GG was based on its great capacity to correctly reduce the list of possible classes. This capacity is measured by a new indicator that we call "the appearance rate". In recent years and due to strong data growth, CNNs have performed classification tasks very well. On the contrary, CG algorithms yield limited results in huge datasets and suffer from high computational time, but they generally reach high appearance rates and do not require any training phase. Consequently, the proposed approach uses an optimal architecture to exploit the advantages of the two techniques and overcome the computational time issue. Experiments carried out on the IFHCDB database have shown that the suggested approach outperforms a normal CNN and yield satisfactory results.

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“…When it comes to particularly Urdu handwritten text so the research has been carried out to a very small extent and the very first script was published in 2004 which is Optical Character Recognition. Urdu handwritten text recognition using image is so called ICR (Intelligent Character Recognition) (1)(2)(3)(4)(5)(6)(7) . However, no robust system is available to date which could produce reliable results.…”

Section: Introductionmentioning

confidence: 99%

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

Chhajro¹

2020

IJST

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Objectives: This research presents a model for Urdu Handwritten Character Recognition via images using various Machine Learning and Deep Learning Techniques. The main objective of this research is to provide comparative study on Urdu Handwritten Characters from images dataset. Methods/Statistical analysis: In this research paper, Support Vector Machine (SVM), K-Nearest Neighbor (K-NN) algorithm, Multi-Layer Perceptron (MLP), Concurrent Neural Network (CNN), Recurrent Neural Network (RNN) and Random Forest Algorithm (RF) have been implemented in order to evaluate most suitable technique for Urdu Handwritten Characters Recognition via images. Findings: Ample amount of research work has been carried out on English Language but it is clearly shown through the conducted literature review that very lesser amount of work has been done on Urdu Handwritten Characters Recognition using images. Furthermore, It has been analyzed from this research that CNN models are most efficient compared to RF, SVM and MLP as to produce reliable results in terms of optimal accuracy. Therefore, using the CNN model is a viable choice to recognize Urdu handwritten characters from the images. And proposed study provides significant contribution in automatic learning of Urdu handwritten Characters.

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Recognition of Urdu Handwritten Characters Using Convolutional Neural Network

Cited by 45 publications

References 48 publications

Recognition of Pashto Handwritten Characters Based on Deep Learning

Recognition of Pashto Handwritten Characters Based on Deep Learning

Towards Directing Convolutional Neural Networks Using Computational Geometry Algorithms: Application to Handwritten Arabic Character Recognition

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

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