Handwritten Urdu character recognition using one-dimensional BLSTM classifier

Ahmed, Saad Bin; Naz, Saeeda; Swati, Salahuddin; Razzak, Muhammad Imran

doi:10.1007/s00521-017-3146-x

Cited by 72 publications

(42 citation statements)

References 22 publications

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“…The best state-of-the-art sequence learner is RNN. The RNN has been adapted to Long Short Term Memory (LSTM) networks, which have been proven to be very useful in Arabic-like text appearing in printed and handwritten script recognition [6,84]. Another very important aspect in relation to Arabic scene text recognition is that the classification techniques based on supervised learning models have not been applied to available datasets.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, for cursive scene text analysis, the focus shifts from traditional backpropagation to context learning classifiers. Recent years' work as explained in [4][5][6]84] on Arabic-like script used the Recurrent Neural Network (RNN) approach for text classification. The RNN is suitable for problems where context is important to learn.…”

Section: Recurrent Neural Networkmentioning

confidence: 99%

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Arabic Cursive Text Recognition from Natural Scene Images

Ahmed

Naz²,

Razzak

et al. 2019

Applied Sciences

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This paper presents a comprehensive survey on Arabic cursive scene text recognition. The recent years’ publications in this field have witnessed the interest shift of document image analysis researchers from recognition of optical characters to recognition of characters appearing in natural images. Scene text recognition is a challenging problem due to the text having variations in font styles, size, alignment, orientation, reflection, illumination change, blurriness and complex background. Among cursive scripts, Arabic scene text recognition is contemplated as a more challenging problem due to joined writing, same character variations, a large number of ligatures, the number of baselines, etc. Surveys on the Latin and Chinese script-based scene text recognition system can be found, but the Arabic like scene text recognition problem is yet to be addressed in detail. In this manuscript, a description is provided to highlight some of the latest techniques presented for text classification. The presented techniques following a deep learning architecture are equally suitable for the development of Arabic cursive scene text recognition systems. The issues pertaining to text localization and feature extraction are also presented. Moreover, this article emphasizes the importance of having benchmark cursive scene text dataset. Based on the discussion, future directions are outlined, some of which may provide insight about cursive scene text to researchers.

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Section: Discussionmentioning

confidence: 99%

Section: Recurrent Neural Networkmentioning

confidence: 99%

Arabic Cursive Text Recognition from Natural Scene Images

Ahmed

Naz²,

Razzak

et al. 2019

Applied Sciences

Self Cite

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“…In 2019 Ahmed et, al [156] proposed a technique based on one-dimensional BLSTM classifier that used recurrent neural network(RNN), long-short term memory(LSTM) and bidirectional recurrent neural networks(BRNN) for the recognition of handwritten Urdu written in Nasta'liq style. Researchers also presented a new dataset of 500 writers named Urdu-Nasta'liq handwritten dataset (UNHD).…”

Section: Urdu Languagementioning

confidence: 99%

Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)

Memon

Sami

Khan³

et al. 2020

IEEE Access

360

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Given the ubiquity of handwritten documents in human transactions, Optical Character Recognition (OCR) of documents have invaluable practical worth. Optical character recognition is a science that enables to translate various types of documents or images into analyzable, editable and searchable data. During last decade, researchers have used artificial intelligence / machine learning tools to automatically analyze handwritten and printed documents in order to convert them into electronic format. The objective of this review paper is to summarize research that has been conducted on character recognition of handwritten documents and to provide research directions. In this Systematic Literature Review (SLR) we collected, synthesized and analyzed research articles on the topic of handwritten OCR (and closely related topics) which were published between year 2000 to 2019. We followed widely used electronic databases by following pre-defined review protocol. Articles were searched using keywords, forward reference searching and backward reference searching in order to search all the articles related to the topic. After carefully following study selection process 176 articles were selected for this SLR. This review article serves the purpose of presenting state of the art results and techniques on OCR and also provide research directions by highlighting research gaps.

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“…In recent years, deep learning networks particularly CNNs have become most common used methods to solve image processing, pattern recognition and several other computer vision problems. These networks have demonstrated state-of-the-art performance for the Arabic and Urdu handwritten character recognition (4)(5)(6) than other methods. Further, CNNs are capable to classify and recognize text at word or character levels without prior information about the structure of the language.…”

Section: Introductionmentioning

confidence: 99%

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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Handwritten Urdu character recognition using one-dimensional BLSTM classifier

Cited by 72 publications

References 22 publications

Arabic Cursive Text Recognition from Natural Scene Images

Arabic Cursive Text Recognition from Natural Scene Images

Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)

Deep learning-based isolated handwritten Sindhi character recognition

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