Ligature categorization based Nastaliq Urdu recognition using deep neural networks

Rafeeq, Muhammad Jawad; Rehman, Zia Ur; Khan, Ahmad; Khan, Iftikhar Ahmed; Jadoon, Waqas

doi:10.1007/s10588-018-9271-y

Cited by 17 publications

(15 citation statements)

References 20 publications

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“…The error rate was 6.04-7.93% during various experiments. During another study, Rafeeq et, al [157] used a deep neural network with dropout regularization. Ligatures were categorized, and the K-Means algorithm is used to cluster the ligatures.…”

Section: Urdu Languagementioning

confidence: 99%

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

Memon

Sami

Khan³

et al. 2020

IEEE Access

314

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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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Section: Urdu Languagementioning

confidence: 99%

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

Memon

Sami

Khan³

et al. 2020

IEEE Access

314

View full text Add to dashboard Cite

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“…It is most commonly used to make analysis of visual images and it is a deep neural networks class. Applications of CNN can be widely found in the fields of image classification, image analysis, image recognition, image prediction, recommendation system, text prediction, video recognition, natural language processing and many more (16) . Moreover, CNN is multilayer perceptron regularized version but in multilayer each neuron connected to every other neuron which makes it fully connected but CNN opts the other technique by regularizing the data.…”

Section: Convolution Neural Network (Cnn)mentioning

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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“…A similar kind of study is done by Ahmed et al [29] and Oulladji et al [30] for Arabic outdoor text. Similarly, numerous studies [3], [31]- [36] exist for the recognition of Urdu and give comparisons to prove the worthiness of their research. Besides custom used datasets [6], there is only one famous Artificial Urdu dataset for text detection, and that is Artificial-Urdu-Text-Dataset [37], that is openly available.…”

Section: Introductionmentioning

confidence: 99%

“…There also exist CLE-18000 [32], [39] which contains near 18K ligatures (compound characters). Others [11], [25], [31], have used custom cropped ligatures for recognition purposes [11].…”

Section: Introductionmentioning

confidence: 99%

Urdu-Text Detection and Recognition in Natural Scene Images Using Deep Learning

Arafat

Iqbal

2020

IEEE Access

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Urdu text is a cursive script and belongs to a non-Latin family of other cursive scripts like Arabic, Chinese, and Hindi. Urdu text poses a challenge for detection/localization from natural scene images, and consequently recognition of individual ligatures in scene images. In this paper, a methodology is proposed that covers detection, orientation prediction, and recognition of Urdu ligatures in outdoor images. As a first step, the custom FasterRCNN algorithm has been used in conjunction with well-known CNNs like Squeezenet, Googlenet, Resnet18, and Resnet50 for detection and localization purposes for images of size 320 × 240 pixels. For ligature Orientation prediction, a custom Regression Residual Neural Network (RRNN) is trained/tested on datasets containing randomly oriented ligatures. Recognition of ligatures was done using Two Stream Deep Neural Network (TSDNN). In our experiments, five-set of datasets, containing 4.2K and 51K Urdu-text-embedded synthetic images were generated using the CLE annotation text to evaluate different tasks of detection, orientation prediction, and recognition of ligatures. These synthetic images contain 132, and 1600 unique ligatures corresponding to 4.2K and 51K images respectively, with 32 variations of each ligature (4-backgrounds and font 8-color variations). Also, 1094 real-world images containing more than 12k Urdu characters were used for TSDNN's evaluation. Finally, all four detectors were evaluated and used to compare them for their ability to detect/localize Urdu-text using average-precision (AP). Resnet50 features based FasterRCNN was found to be the winner detector with AP of.98. While Squeeznet, Googlenet, Resnet18 based detectors had testing AP of.65, .88, and .87 respectively. RRNN achieved and accuracy of 79% and 99% for 4k and 51K images respectively. Similarly, for characters classification in ligatures, TSDNN attained a partial sequence recognition rate of 94.90% and 95.20% for 4k and 51K images respectively. Similarly, a partial sequence recognition rate of 76.60% attained for real world-images. INDEX TERMS BLSTM, deep neural network, FasterRCNN, image classification, Nastalique, optical character recognition (OCR), regression residual neural network (RRNN), synthetic urdu text, text detection, two stream deep neural network (TSDNN).

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Ligature categorization based Nastaliq Urdu recognition using deep neural networks

Cited by 17 publications

References 20 publications

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

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

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

Urdu-Text Detection and Recognition in Natural Scene Images Using Deep Learning

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