Handwritten Sindhi Character Recognition Using Neural Networks

Awan, Shafique Ahmed; Abro, Z. H.; Jalbani, Akhtar Hussain; Hakro, Dil Nawaz; Hameed, M. Syed Shahul

doi:10.22581/muet1982.1801.17

Cited by 4 publications

(6 citation statements)

References 3 publications

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“…To the best of authors' knowledge, this is the first research proposing deep learning method for the handwritten Sindhi character recognition. The performance of the proposed method is hence compared with existing methods as reported in (9)(10)(11)13) . In (9) a zoning method was used to extract the features from the segmented Sindhi characters and an artificial neural network was applied for the classification.…”

Section: Performance Comparison With Previously Published Resultsmentioning

confidence: 99%

“…A significant research work has been performed for the Latin, Indian, Chinse, Urdu or Arabic scripts (7,8) , however the development of Sindhi OCR is still in a preliminary stage and has not shown much improvements. Although, some research has been reported for the Sindhi handwritten character recognition (9)(10)(11)(12)(13) , but the recognition accuracy is not state-of-the-art.…”

Section: Introductionmentioning

confidence: 99%

“…Awan et al (9) proposed a neural network-based method to recognize handwritten Sindhi characters. Handwritten character samples collected were scanned and converted into binary images.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning-based isolated handwritten Sindhi character recognition

Chandio¹,

Leghari²,

Panhwar³

et al. 2020

IJST

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Motivation : The problem of handwritten text recognition is vastly studied since last few decades. Many innovative ideas have been developed, where state-of-the-art accuracy is achieved for the English, Chinese or Indian scripts. The recent developments for the cursive scripts such as Arabic and Urdu handwritten text recognition have achieved remarkable accuracy. However, for the Sindhi script, existing systems have not shown significant results and the problem is still an open challenge. Several challenges such as variations in writing styles, joined text, ligature overlapping, and others associated to the handwritten Sindhi text make the problem more complex. Objectives: In this study, a deep residual network with shortcut connections and summation fusion method using convolutional neural network (CNN) is proposed for automatic feature extraction and classification of handwritten Sindhi characters. Method: To increase the powerful feature representation ability of the network, the features of the convolutional layers in the residual block are fused together and combined with the output of the previous residual block. The proposed network is trained on a custom developed handwritten Sindhi character dataset. To tackle the problem of small data, a data augmentation with rotation, flipping and image enhancement techniques have been used. Findings: The experimental results show that the proposed model outperforms than the best results previously published for the handwritten Sindhi character recognition. Novelty: This is the first research that proposes deep residual network with summation fusion for the Sindhi handwritten text recognition.

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Section: Performance Comparison With Previously Published Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Deep learning-based isolated handwritten Sindhi character recognition

Chandio¹,

Leghari²,

Panhwar³

et al. 2020

IJST

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show abstract

“…A lot of work is in English script and NLP tools are offered in English scripts which perform all tasks of English script, but in the Sindhi language, no powerful application is available for the feature extraction and corpus. Sindhi is the right-handed written script that is as the Arabic and Urdu [9]. But the usage of Sindhi script is increasing at every platform, especially in social media, text communication is also used in various sources (online magazines, newspapers, poetry, learning websites of Sindhi) etc.…”

Section: Related Workmentioning

confidence: 99%

Romanized Sindhi Rules for Text Communication

Sodhar

Jalbani

Channa

et al. 2021

Mehran Univ. res. j. eng. technol.

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Sindhi is one of the historical languages which is widely used in all over the world, but especially in the province of Sindh Pakistan. Sindhi language has its own script and written by the right-handed. Nowadays the use of different Sindhi platforms is increasing especially for communication. The majority of the people of Sindh province read, write and speak very well, but they face the problem in text communication while using different communication platforms. However, the users of computer and mobile phone feel trouble/difficulty during the use of the Sindhi script in typing of text messages, tweets and comments while using different platforms in computer and mobile phone. Natural Language Processing (NLP) is one of the better options for the solution of these problems of text communication on different platforms. For the proper solution of text communication issues, Romanized Sindhi text is used instead of Sindhi text. Romanized text writing is easier than the Sindhi text writing because Sindhi text writing needs the special type of keyboard while writing of Romanized text does not need any special type of keyboard. For the writing of Romanized Sindhi text, rules are defined in this paper which provide easiness during writing and understanding of the text. Romanized Sindhi Rules (RSR) are simple and easy to understand the meaning of the text and provide fast communication (text). This study is also helpful for further research in the Romanized Sindhi text by using different approaches and provides easiness in communication.

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“…They also performed feature dimensionality reduction on the datasets. Shafique et al [14] suggested the concept of neural network for the recognition of handwritten Sindhi characters. Naz et al [15] proposed entity recognition system in Urdu language using hybrid unigram and bigram approaches based on IJCNLP NE dataset and CRL NE dataset.…”

Section: Related Workmentioning

confidence: 99%

Zernike Moments Based Handwritten Pashto Character Recognition Using Linear Discriminant Analysis

Jehangir

Khan

et al. 2021

Mehran Univ. res. j. eng. technol.

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This paper presents an efficient Optical Character Recognition (OCR) system for offline isolated Pashto characters recognition. Developing an OCR system for handwritten character recognition is a challenging task because of the handwritten characters vary both in shape and in style and most of the time the handwritten characters also vary among the individuals. The identification of the inscribed Pashto letters becomes even palling due to the unavailability of a standard handwritten Pashto characters database. For experimental and simulation purposes a handwritten Pashto characters database is developed by collecting handwritten samples from the students of the university on A4 sized page. These collected samples are then scanned, stemmed and preprocessed to form a medium sized database that encompasses 14784 handwritten Pashto character images (336 distinguishing handwritten samples for each 44 characters in Pashto script). Furthermore, the Zernike moments are considered as a feature extractor tool for the proposed OCR system to extract features of each individual character. Linear Discriminant Analysis (LDA) is followed as a recognition tool for the proposed recognition system based on the calculated features map using Zernike moments. Applicability of the proposed system is tested by validating it with 10-fold cross-validation method and an overall accuracy of 63.71% is obtained for the handwritten Pashto isolated characters using the proposed OCR system.

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Handwritten Sindhi Character Recognition Using Neural Networks

Cited by 4 publications

References 3 publications

Deep learning-based isolated handwritten Sindhi character recognition

Deep learning-based isolated handwritten Sindhi character recognition

Romanized Sindhi Rules for Text Communication

Zernike Moments Based Handwritten Pashto Character Recognition Using Linear Discriminant Analysis

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