Detection and recognition of cursive text from video frames

Mirza, Ali; Zeshan, Ossama; Atif, Muhammad; Siddiqi, Imran

doi:10.1186/s13640-020-00523-5

Cited by 14 publications

(6 citation statements)

References 115 publications

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“…Experiments are conducted on 12,000 text lines culled from 4,000 video frames from Pakistani news stations. Reference [7] proposed a comparable UrduNet model, which is a hybrid approach of CNN and LSTM. On a self-generated dataset of almost 13,000 frames, a complete set of experiments are carried out.…”

Section: Background and Related Workmentioning

confidence: 99%

See 1 more Smart Citation

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%

“…Mirza et al [7] present an implicit technique for new tickers from video frames by combining CNN and LSTM. This can be seen that even on its own training dataset, the model has difficulty in training with low accuracy.…”

Section: Explicit Segmentationmentioning

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

“…Naz et al [27] extracts features with CNN by the usage of MNIST dataset and does not exactly contemplate Urdu text features. In addition, as mentioned prior [14] Experiments and results along with comparisons with existing systems for text recognition are presented in Section IV, whereas section V concludes the study.…”

Section: ) Explicit Segmentationmentioning

confidence: 99%

Recognition of Visual Arabic Scripting News Ticker From Broadcast Stream

et al. 2022

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News ticker recognition is a vital area of research due to its applications such as information analysis, opinion mining and language translation for media regulatory authorities. Without automated systems, manual anatomizing is difficult. In this paper, we focus on the automatic Arabic and Urdu news ticker recognition system. It mainly consists of ticker segmentation and text recognition to generate textual data for various online services. Our work investigates character-wise explicit segmentation and syntactical models with Kufi and Nastaleeq fonts. Various network models anticipate learning of deep representations by homogenizing the classes regardless of inter-symbol correlations and linguistic taxonomy. The proposed learning model incorporates fairness by maximizing the balance among sensitive features of characters in a unified manner. Furthermore, we demonstrate the efficiency of the proposed model by carrying out experiments using customized news tickers datasets with accurate character-level and component-level labeling. Moreover, our method is evaluated on a challenging Urdu Printed Text Images (UPTI) dataset that only provides ligature based annotations. The proposed method attains 98.36%, outperforms the current state of the art method. Ablation investigations show that our technique enhances the performance of character classes with low symbol frequencies.

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“…Tesseract is an open-source OCR engine it takes images that attempt to acknowledge the text. The result is a text string, and the degree to which an image is similar to a human-readable text is measured by its correctness [17]. The OCR is used to recognize printed documents in papers, handwritten characters, or physical text messages such as license plates, street signs, and street numbers, document account holders, legal forms, ID cards, and so on.…”

Section: Feature Extractionmentioning

confidence: 99%