Two Stream Deep Neural Network for Sequence-Based Urdu Ligature Recognition

Arafat, Syed Yasser; Iqbal, Muhammad

doi:10.1109/access.2019.2950537

Cited by 11 publications

(7 citation statements)

References 45 publications

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“…After preprocessing the recognition target, the main text is determined, and the text growth method is used to extract the Manchu strokes automatically; the stroke extraction accuracy was 92.38%, and the stroke recognition rate was 92.22%. In 2019, Arafat and Iqbal et al worked on the recognition of handwritten Manchu text [19]. First, the handwritten like Manchu text is scanned, and the image is preprocessed.…”

Section: Manchu Language Ocrmentioning

confidence: 99%

OCR with the Deep CNN Model for Ligature Script-Based Languages like Manchu

Zhang

Liu

Wang

et al. 2021

Scientific Programming

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Manchu is a low-resource language that is rarely involved in text recognition technology. Because of the combination of typefaces, ordinary text recognition practice requires segmentation before recognition, which affects the recognition accuracy. In this paper, we propose a Manchu text recognition system divided into two parts: text recognition and text retrieval. First, a deep CNN model is used for text recognition, using a sliding window instead of manual segmentation. Second, text retrieval finds similarities within the image and locates the position of the recognized text in the database; this process is described in detail. We conducted comparative experiments on the FAST-NU dataset using different quantities of sample data, as well as comparisons with the latest model. The experiments revealed that the optimal results of the proposed deep CNN model reached 98.84%.

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Section: Manchu Language Ocrmentioning

confidence: 99%

OCR with the Deep CNN Model for Ligature Script-Based Languages like Manchu

Zhang

Liu

Wang

et al. 2021

Scientific Programming

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“…An overlap ratio of 0.5 to 1 is used as a positive sample for learning. The anchor boxes which are chosen for training for all FasterRCNNs were [67], [57]; [136,116]; [272,232]. The hold-out ratio was kept 0.9, resulting in 3801 images as a train-set and 423 images as test-set.…”

Section: B Custom Feature Fasterrcnn Buildermentioning

confidence: 99%

“…The Two Stream Deep Neural Network (TSDNN) module in the proposed methodology is inspired by [67], with the difference of handling color images, number of features, and using CNN features found to robust for Urdu text detection, i.e., googlenet and resnet50. These two CNNs are selected based on their detection performance in the detection step in our experimentation.…”

Section: F Ligature Recognitionmentioning

confidence: 99%

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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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“…Several approaches have been proposed for typewritten Urdu ligature recognition [63], [64]. This type of text is usually horizontally aligned, written with one font type, font size and on a plain/clean background.…”

Section: Ieee Accessmentioning

confidence: 99%

Cursive Character Recognition in Natural Scene Images Using a Multilevel Convolutional Neural Network Fusion

2020

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The accuracy of current natural scene text recognition algorithms is limited by the poor performance of character recognition methods for these images. The complex backgrounds, variations in the writing, text size, orientations, low resolution and multi-language text make recognition of text in natural images a complex and challenging task. Conventional machine learning and deep learning-based methods have been developed that have achieved satisfactory results, but character recognition for cursive text such as Arabic and Urdu scripts in natural images is still an open research problem. The characters in the cursive text are connected and are difficult to segment for recognition. Variations in the shape of a character due to its different positions within a word make the recognition task more challenging than non-cursive text. Optical character recognition (OCR) techniques proposed for Arabic and Urdu scanned documents perform very poorly when applied to character recognition in natural images. In this paper, we propose a multiscale feature aggregation (MSFA) and a multi-level feature fusion (MLFF) network architecture to recognize isolated Urdu characters in natural images. The network first aggregates multi-scale features of the convolutional layers by up-sampling and addition operations and then combines them with the high-level features. Finally, the outputs of the MSFA and MLFF networks are fused together to create more robust and powerful features. A comprehensive dataset of segmented Urdu characters is developed for the evaluation of the proposed network models. Synthetic text on the patches of images with real natural scene backgrounds is generated to increase the samples of infrequently used characters. The proposed model is evaluated on the Chars74K and ICDAR03 datasets. To validate the proposed model on the new Urdu character image dataset, we compare its performance with the histogram of oriented gradients (HoG) method. The experimental results show that the aggregation of multi-scale and multilevel features and their fusion is more effective, and outperforms other methods on the Urdu character image and Chars74K datasets. INDEX TERMS Cursive text recognition, natural scene Urdu character recognition, multi-scale feature aggregation, multi-level feature fusion, convolutional neural network (CNN)

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Two Stream Deep Neural Network for Sequence-Based Urdu Ligature Recognition

Cited by 11 publications

References 45 publications

OCR with the Deep CNN Model for Ligature Script-Based Languages like Manchu

OCR with the Deep CNN Model for Ligature Script-Based Languages like Manchu

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

Cursive Character Recognition in Natural Scene Images Using a Multilevel Convolutional Neural Network Fusion

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