Impact of Ligature Coverage on Training Practical Urdu OCR Systems

Naeem, M. Asif; Zia, Noor ul Sehr; Awan, Aqsa Ahmed; Hasan, Adnan ul

doi:10.1109/icdar.2017.30

Cited by 7 publications

(3 citation statements)

References 16 publications

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“…The dataset consists of approximately 10,000 text lines, which are insufficient to train a reasonably good handwriting text recognition engine.The standard method of increasing the training samples is to introduce data augmentation; however, we argue that data augmentation methods are not helpful in training a text recognition system. In[22], we showed that ligature coverage has a positive impact in improving the accuracy of a text recognition system. It is specifically true for Arabic like scripts where the number of ligatures are huge.…”

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confidence: 93%

Conv-transformer architecture for unconstrained off-line Urdu handwriting recognition

Riaz

Arbab

Maqsood

et al. 2022

IJDAR

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Unconstrained off-line handwriting text recognition in general and for Arabic-like scripts in particular is a challenging task and is still an active research area. Transformer based models for English handwriting recognition have recently shown promising results. In this paper, we have explored the use of transformer architecture for Urdu handwriting recognition. The use of a Convolution Neural Network before a vanilla full Transformer and using Urdu printed text-lines along with handwritten text lines during the training are the highlights of the proposed work. The Convolution Layers act to reduce the spatial resolutions and compensate for the n 2 complexity of transformer multi-head This research is partially funded by Higher Commission (HEC), Pakistan's grant for the National Center of Artificial Intelligence (NCAI).

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confidence: 93%

Conv-transformer architecture for unconstrained off-line Urdu handwriting recognition

Riaz

Arbab

Maqsood

et al. 2022

IJDAR

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“…From the literature, we noticed that mostly the transfer learned networks are effectively smeared on the printed script rather than handwritten, and we also observe that CNN-based transfer learning is effectively applied to the non-cursive script like Chinese, Latin, Bangala, and Devanagari, etc. Relatively very few research have been concentrated on cursive scripts like Arabic [18], Urdu [19], and Farsi [20], where the experiment carried out on regular documents, and the network models are trained using specific benchmark datasets like UNHD [21], UPTI [22], EMILLE [23] and WordNet [24]. These datasets consist of handwritten text lines and word images written by various writers.…”

Section: Introductionmentioning

confidence: 99%

Multi-Domain Deep Convolutional Neural Network for Ancient Urdu Text Recognition System

Aarif¹,

Sivakumar²

2022

Intelligent Automation &Amp; Soft Computing

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Deep learning has achieved magnificent success in the field of pattern recognition. In recent years Urdu character recognition system has significantly benefited from the effectiveness of the deep convolutional neural network. Majority of the research on Urdu text recognition are concentrated on formal handwritten and printed Urdu text document. In this paper, we experimented the Challenging issue of text recognition in Urdu ancient literature documents. Due to its cursiveness, complex word formation (ligatures), and context-sensitivity, and inadequate benchmark dataset, recognition of Urdu text from the literature document is very difficult to process compared to the formal Urdu text document. In this work, first, we generated a dataset by extracting the recurrent ligatures from an ancient Urdu fatawa book. Secondly, we categorized and augment the ligatures to generate batches of augmented images that improvise the training efficiency and classification accuracy. Finally, we proposed a multi-domain deep Convolutional Neural Network which integrates a spatial domain and a frequency domain CNN to learn the modular relations between features originating from the two different domain networks to train and improvise the classification accuracy. The experimental results show that the proposed network with the augmented dataset achieves an averaged accuracy of 97.8% which outperforms the other CNN models in this class. The experimental results also show that for the recognition of ancient Urdu literature, well-known benchmark datasets are not appropriate which is also verified with our prepared dataset.

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“…While for recognition of Urdu characters from outdoor images there are few custom datasets [11], [15], [25] and for recognition of printed characters words there is a famous dataset UPTI [24], which recently has been updated and has been presented with name UPTI2.0 [38] because the performance on UPTI has reached near saturation [33], [35]. There also exist CLE-18000 [32], [39] which contains near 18K ligatures (compound characters).…”

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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Impact of Ligature Coverage on Training Practical Urdu OCR Systems

Cited by 7 publications

References 16 publications

Conv-transformer architecture for unconstrained off-line Urdu handwriting recognition

Conv-transformer architecture for unconstrained off-line Urdu handwriting recognition

Multi-Domain Deep Convolutional Neural Network for Ancient Urdu Text Recognition System

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

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