Impact of Pre-Processing on Recognition of Cursive Video Text

Mirza, Ali; Siddiqi, Imran; Mustufa, Syed Ghulam; Hussain, Mazahir

doi:10.1007/978-3-030-31332-6_49

Cited by 6 publications

(2 citation statements)

References 25 publications

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“…Reference [5] demonstrated a rudimentary work using Bidirectional Long Short-Term Memory (BLSTM) to recognize Urdu News Ticker. Reference [6] suggested a deep learning model based on a Convolutional Neural Network (CNN) and LSTM combination. Experiments are conducted on 12,000 text lines culled from 4,000 video frames from Pakistani news stations.…”

Section: Background and Related Workmentioning

confidence: 99%

Visual News Ticker Surveillance Approach from Arabic Broadcast Streams

Tayyab¹,

Hussain²,

Mir³

et al. 2023

Computers, Materials &Amp; Continua

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The news ticker is a common feature of many different news networks that display headlines and other information. News ticker recognition applications are highly valuable in e-business and news surveillance for media regulatory authorities. In this paper, we focus on the automatic Arabic Ticker Recognition system for the Al-Ekhbariya news channel. The primary emphasis of this research is on ticker recognition methods and storage schemes. To that end, the research is aimed at character-wise explicit segmentation using a semantic segmentation technique and words identification method. The proposed learning architecture considers the grouping of homogeneousshaped classes. This incorporates linguistic taxonomy in a unified manner to address the imbalance in data distribution which leads to individual biases. Furthermore, experiments with a novel Arabic News Ticker (Al-ENT) dataset that provides accurate character-level and character components-level labeling to evaluate the effectiveness of the suggested approach. The proposed method attains 96.5%, outperforming the current state-of-the-art technique by 8.5%. The study reveals that our strategy improves the performance of lowrepresentation correlated character classes.

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Section: Background and Related Workmentioning

confidence: 99%

Visual News Ticker Surveillance Approach from Arabic Broadcast Streams

Tayyab¹,

Hussain²,

Mir³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Much of the current research on Urdu recognition is performed on the cleaned and segmented artificially generated Urdu Nastaliq text such as Urdu Printed Text Images (UPTI) [24], custom extracted [15], generated text with clear background [25], video tickers [26] or handwritten Urdu text [27] as opposed to extracting from outdoor or real-world images with complex background. This work is a step in that direction that integrates synthetic Urdu-text in natural outdoor images.…”

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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