## 2 Literature Review
[26] used contour extraction techniques and shape context information to create attribute descriptors for each ligature/glyph. Similarly, [27] used morphological operations and character-specific filters to pre-process each segmented character/glyph from a line image. They used a heuristics-based approach on the character-chain-code to determine which class label (Urduglyph) the segmented image is assigned. CNN takes input data, extracts low-level features, and then is fed to MDLSTM. All resulting vectors are concatenated to form a single vector to be passed on to Connectionist Temporal Classification (CTC) transcript layer [28]. The supervised learning methods for text recognition [29; 30] will generally be more complex than the masked procedures and recognize text and non-text blocks. AI approaches are thought to be among the most recently accomplished approaches, an Artificial Neural Network (ANN) is seen in [31]. These AI-based methodologies require a ton of preparing information to increase adequate order rates. A Character Proposal Network (CPN) for looking through character recommendations is utilized for text ID in [32] though a lot of mid-level natives to get the segmental structure of characters (named as \"stroke lets\") is in [28]. Commonly, unsupervised methodologies are characterized by edge-based, CCA-based, surface-based, and shading-based strategies. In unsupervised techniques, picture assessment approaches are placed in, and segment strategies (edges, spatial gathering) are utilized to recognize text from the remainder of the picture. Surface-based strategies [33; 34] consider literary substance in the picture as an exceptional surface that separates itself from the non-text parts. Surface credits are assessed from dark-level pictures or by first changing the picture by separating or applying recurrence space changes. Numerous investigations [35] endeavor various textural measures to see and affirm text parts in a picture. Edge-based methodologies [36] use the high difference between text and its experience by looking through the image's edges. Fragments of high edge thickness are then consolidated under specific heuristics to sift through non-text areas. Associated segment-based techniques [37; 38] endeavor the shading/power of text pixels alongside some mathematical heuristics to separate content from the foundation. Multilingual pretraining can generally enhance a model's monolingual performance, it has been demonstrated that as a multilingual model learns more languages, the capacity for each language falls off [39]. Recent research has demonstrated that multilingual language models perform worse than their monolingual equivalents [40]. A detail of challenges in multilingual models are explained in detail [41]. Even bilingual language modeling has been found to perform better than multilingual modeling [42]. A study shows that monolingual versions outperform the traditional multilingual models for all datasets. Moreover, better sentence representations are also generated by the monolingual models [43]. However, training and maintenance for each language in a monolingual model is not cost-effective in terms of time and resources. Urdu being a resource-starved language, large pre-trained language models cannot be built on a single corpus. A significant amount of work has previously been done on object detection in photos using deep neural network [44]. In another study, fast text classification is performed by processing only the text part of the image instead of the whole picture [45]. Considering text as object, the existing methodologies can be applied for text detection. This concept is mainly followed in the detection and recognition of text in different languages including English [46; 47; 48; 49], Chinese [50; 51; 52], Arabic [53; 54], Persian [55; 56], Urdu [57; 58; 59; 60] and Hindi [61; 62; 63]. In some languages (including Urdu), there exists multiple ligatures and orthography.