This paper gives an across-the-board comprehensive review and survey of the most prominent studies in the field of Urdu optical character recognition (OCR). This paper introduces the OCR technology and presents a historical review of the OCR systems, providing comparisons between the English, Arabic, and Urdu systems. Detailed background and literature have also been provided for Urdu script, discussing the script's past, OCR categories, and phases. This paper further reports all state-of-the-art studies for different phases, namely, image acquisition, pre-processing, segmentation, feature extraction, classification/recognition, and post-processing for an Urdu OCR system. In the segmentation section, the analytical and holistic approaches for Urdu text have been emphasized. In the feature extraction section, a comparison has been provided between the feature learning and feature engineering approaches. Deep learning and traditional machine learning approaches have been discussed. The Urdu numeral recognition systems have also been deliberated concisely. The research paper concludes by identifying some open problems and suggesting some future directions.

In the current dynamic market, businesses have recognized the pivotal role of data and sustainability technologies in attaining competitive advantage. Big Data Analytics-Artificial Intelligence and Green Supply Chain Management are significant sustainability promotion strategies. The research collected data from 220 employees in the Taiwanese manufacturing sector with the help of a survey methodology. The findings revealed significant impacts of Big Data Analytics-Artificial Intelligence on both green supply chain management and supply chain ambidexterity. Moreover, supply chain ambidexterity significantly influences green supply chain management. Lastly, supply chain ambidexterity was also found to mediate the relationship between Big Data Analytics-Artificial Intelligence and green supply chain management. This study provides several implications for fostering a responsible economy. It elucidates how leveraging Big Data Analytics-Artificial Intelligence enhances supply chain ambidexterity, reinforcing sustainable practices without detectable alterations.

<!-- *** Custom HTML *** --> Over the past decades, text recognition technologies have focused immensely on noncursive isolated scripts. A text recognition system for the cursive Pashto script will serve as a great contribution, allowing the traditional, cultural, and educational Pashto literature to be converted into machine-readable form. We propose the use of deep learning architectures based on the transfer learning for the recognition of Pashto ligatures. For recognition analysis and evaluation, the ligature images in the dataset are preprocessed by data augmentation techniques, i.e., negatives, contours, and rotated to increase the variation of each sample and size of the original dataset. Rich feature representations are automatically extracted from the Pashto ligature images using deep convolution layers of the convolution neural network (CNN) architectures using fine-tuned approach. Pretrained CNN architectures: AlexNet, GoogleNet, and VGG (VGG-16 and VGG-19) are used for classification by feeding the extracted features to a fully connected layer and a softmax layer. The proposed deep transfer-based learning has achieved phenomenal recognition rates for Pashto ligatures on benchmark FAST-NU Pashto dataset. An accuracy of 97.24%, 97.46%, and 99.03% is achieved using AlexNext, GoogleNet, and VGGNet architectures, respectively.

Cursive text recognition of Arabic script-based languages like Urdu is extremely complicated due to its diverse and complex characteristics. Evolutionary approaches like genetic algorithms have been used in the past for various optimization as well as pattern recognition tasks, reporting exceptional results. The proposed Urdu ligature recognition system uses a genetic algorithm for optimization and recognition. Overall the proposed recognition system observes the processes of pre-processing, segmentation, feature extraction, hierarchical clustering, classification rules and genetic algorithm optimization and recognition. The pre-processing stage removes noise from the sentence images, whereas, in segmentation, the sentences are segmented into ligature components. Fifteen features are extracted from each of the segmented ligature images. Intra-feature hierarchical clustering is observed that results in clustered data. Next, classification rules are used for the representation of the clustered data. The genetic algorithm performs an optimization mechanism using multi-level sorting of the clustered data for improving the classification rules used for recognition of Urdu ligatures. Experiments conducted on the benchmark UPTI dataset for the proposed Urdu ligature recognition system yields promising results, achieving a recognition rate of 96.72%.