Urdu Caption Text Detection using Textural Features

Mirza, Ali; Fayyaz, Marium; Seher, Zunera; Siddiqi, Imran

doi:10.1145/3177148.3180098

Cited by 8 publications

(4 citation statements)

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“…Detection of Urdu-text is challenging due to some of the following problems like types of scripts (Nastaliq, Naskh, handwritten, hybrid, etc. ), scale, large variance of text patterns, oriented text, complicated background, color, contrast, perspective distortions, image quality and variation of text size [4]- [6]. Figure 1 shows key challenges in the detection of Urdu-Text/Script.…”

Section: Introductionmentioning

confidence: 99%

“…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]. While regarding Urdu script orientation determination no well-known study exists.…”

Section: Introductionmentioning

confidence: 99%

“…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. It consists of 1000 video frames from news channels and is openly available.…”

Section: Introductionmentioning

confidence: 99%

“…The number of images in English datasets varies from 500 to more than 60K. Although researchers have mentioned Urdu text datasets [6], [15], [16], [37] for detection and recognition, only one dataset [37] is publicly available for text detection in video frame images. Also, scholars [45], [46] have done bilingual recognition on MSRA-TD [43] and other datasets.…”

Section: Introductionmentioning

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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Section: Introductionmentioning

confidence: 99%