## 2 Literature Review  
Several studies have been conducted on script recognition and mostly English text recognition. However, many people have not been able to perform Urdu research or identify every aspect of Urdu writing. The following section briefly reviews some research on Urdu text recognition. Segmentation of text to characters explicitly is a highly challenging task in cursive scripts like Urdu. Research is carried out by segmenting text into characters implicitly with the help of deep learning. The writing on Urdu text recognition can be arranged dependent on the element of recognition, for example, characters or ligatures. The research network has created two benchmark datasets of printed Urdu text, the Urdu Printed Text Line Images (UPTI) dataset [4; 5] and the Center of Language Engineering (CLE) dataset. The UPTI dataset consists of 1000 lines of text, while the CLE dataset consists of more than 2000 groups of high-recurrence Urdu ligatures and scanned pages of books. Reflecting on the strategies assessed on the CLE dataset, [PERSON] et al. subdivide the ligatures at spreading points. Raw pixel values are used to train Hidden Markov Models (HMMs) on each fragment of ligatures. Assessments are done on 1692 essential ligatures with correctness of 92%. This work is expanded in [6; 7; 8], in which 250 graphemes are used to train HMMs. In [9], a detailed identification proposal is presented, which uses Discrete cosine transform (DCT) features and HMMs. They have modified the Tesseract engine to include recognition of Urdu ligatures, realizing around a 97% recognition rate for ligatures in fixed font sizes. In [10], a test on 6000 queries, the ligatures realized an identification rate of 97.93%, and arithmetic features are taken from the 2028 HFL clusters. The primary and secondary ligatures association was also carried out. Among different identification techniques, [PERSON] and [PERSON] in [11] use object descriptors to recognize ligatures in the UPTI database, resulting in an 89% recognition rate. On the UPTI database [12; 13; 14], many implicit partition-based techniques have also been calculated. In such methods, the learning limitations are determined after the ground truth transcription, and the pictures of text lines are provided to the learning algorithm. Refs. [15; 16] used statistical method with multi-dimensional Recurrent Neural Networks (MD-RNN), whereas [17; 18] used bidirectional Long Short-Term Memory (LSTM) networks on raw pixel values. The background is that the ligature-based approaches evaluated on the UPTI database are lesser because the database is labeled at line level, and ligatures have to be manually taken from the database. The study [19] concludes a 95% classification rate on around 100 ligature classes. In [20; 21], statistical characteristics are used with HMMs to identify more than 1500 ligatures classes in the UPTI database. From the outlook of video text identification, the research on cursive scripts is limited. Few video OCRs targeting recognition of Arabic text have been reported in the literature [22]. In [23], the authors deployed deep learning-based methods for identifying Arabic video text. Three classifiers, including deep belief networks, multilayer perceptron, and deep autoencoder, realized recognition rates of 90.73%, 88.5%, and 94.36%, respectively. The procedure was calculated using a database with 47 h of video. The studies [8; 24] stated an approach for noticing, localization, and recognizing Urdu text in videos. Identification rates of around 91-93% were obtained. The system is evaluated on 10 h of video, each from three different channels. A partition-based technique for OCR using a Support Vector Machine (SVM) was proposed [25]. They evaluated local and global features on top of partitioned cursive characters. Most of the work in the domain of Urdu OCR consumed handcrafted attributes and used the nearest-adjacent approach to perform identification. Ref.