Character classification and recognition for Urdu texts in natural scene images

Chandio, Asghar Ali; Pickering, Mark; Shafi, Kamran

doi:10.1109/icomet.2018.8346341

Cited by 22 publications

(17 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is the first benchmark for Arabic character recognition in natural images. A baseline research work has been done by [12] character can have more than one shape such as: initial, middle, final or isolated as shown in ( (Fig 1. a), (Fig 1. b), (Fig 1. c) and (Fig 1. d).…”

Section: A Database For Urdu Text Detection and Recognition In Naturamentioning

confidence: 99%

A Database for Urdu Text Detection and Recognition in Natural Scene Images

Leghari

Memon

Leghari

et al. 2020

Mehran Univ. res. j. eng. technol.

View full text Add to dashboard Cite

Conventional solar cells are not economical and are recently too expensive to the manufacturers for extensive-scale electricity generation. Cost and efficiency is most vital factor in the accomplishment of any solar technology. In order to improve the conversion efficiency, the major research in third generation photovoltaic (PV) cells is directed toward retaining more sunlight using nanotechnology. Advancement in nanotechnology solar cell via quantum dots (QDs) could reduce the cost of PV cell and additionally enhance cell conversion efficiency. Silicon quantum dots (Si-QDs) are semiconductor nano crystals of nanometers dimension whose electron-holes are confined in all three spatial dimensions. Quantum dots have discrete electronic states. Quantum dots have capacity to change band gap with the adjustment in size of quantum dot. As the quantum dots size fluctuates over a wide range that demonstrates the variety of band gap so it will assimilate or discharge light. In this paper, the generic mathematical models of PV cell are adopted and then I-V and P-V characteristic curves are obtained from selected parameters using MATLAB software. The essential parameters are taken from datasheets. I-V and P-V characteristics curves are obtained for selected model. Silicon quantum dots have the tunable band gap that is added to conventional PV cell and obtain the I-V and P-V curves. After simulation, efficiency and power of Conventional PV cell to quantum dots based PV cell is compared. The property of quantum dots is used in extending the band gap of solar cells and increasing the maximum proportion of incident sunlight absorbed, hence improving efficiency.

show abstract

Section: A Database For Urdu Text Detection and Recognition In Naturamentioning

confidence: 99%

A Database for Urdu Text Detection and Recognition in Natural Scene Images

Leghari

Memon

Leghari

et al. 2020

Mehran Univ. res. j. eng. technol.

View full text Add to dashboard Cite

show abstract

“…Detecting text (especially English and Chinese) in an outdoor environment has been done extensively [7]- [10]. Recently, little work has been done on outdoor Urdu-text detection in images [11] along with Arabic text detection [12]. Although, there are research studies on artificial Urdu-text detection in video frames [4]- [6], [13]- [17].…”

Section: Introductionmentioning

confidence: 99%

“…For pure outdoor Urdu-text detection, we were only able to find one relevant study [16]. Chandio et al [11] and Ali et al [28] used manually cropped Urdu characters from natural outdoor scenes for recognition of Urdu-characters, while in [16] Asghar et al used custom annotated Urdu images with ICDAR2017-MLT Arabic text for detection purpose, they used a combination of their custom collection of Urdu images and Arabic images from ICDAR 2017-MLT [7], They have dealt with Urdu and Arabic text at the word level. A similar kind of study is done by Ahmed et al [29] and Oulladji et al [30] for Arabic outdoor text.…”

Section: Introductionmentioning

confidence: 99%

“…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%

“…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%

See 2 more Smart Citations

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

Arafat

Iqbal

2020

IEEE Access

View full text Add to dashboard Cite

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).

show abstract

TraffSign: Multilingual Traffic Signboard Text Detection and Recognition for Urdu and English

Butt

Ul-Hasan

2022

Document Analysis Systems

View full text Add to dashboard Cite

Character classification and recognition for Urdu texts in natural scene images

Cited by 22 publications

References 14 publications

A Database for Urdu Text Detection and Recognition in Natural Scene Images

A Database for Urdu Text Detection and Recognition in Natural Scene Images

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

TraffSign: Multilingual Traffic Signboard Text Detection and Recognition for Urdu and English

Contact Info

Product

Resources

About