## 1 Introduction  
Image captioning, situated at the crossroads of computer vision and natural language processing, represents an intriguing challenge: to create systems capable of understanding and articulating the narrative of a visual scene. This field has undergone a transformative journey with the introduction of deep learning technologies [1, 2]. In the case of languages with abundant resources like English, image captioning systems have achieved remarkable strides. These systems, powered by extensive annotated datasets and innovative neural network designs, have reached notable levels of precision and coherence [3, 4]. The prevalent integration of Convolutional Neural Networks (CNNs) for image analysis and Recurrent Neural Networks (RNNs) or transformers for text generation has been central to these advancements. These systems' capabilities, transforming pixels into comprehensible narratives, have unlocked a spectrum of applications. From aiding visually impaired individuals in perceiving their environment to enhancing user engagement on digital platforms with automated image descriptions, the potential uses are vast and varied. The synergy between extracting key visual elements and integrating them into linguistically and contextually rich descriptions continues to be an active and expanding field of research, pushing the boundaries of what artificial intelligence can achieve. However, this narrative takes a different turn when considering low-resource languages like Tamil. The challenges here are multifaceted. One primary obstacle is the stark scarcity of extensive, annotated datasets [5]. Unlike English, where data for image-caption pairs is plentiful and diverse, Tamil faces a dearth of such resources, impeding the development of comprehensive models. Furthermore, the distinctive linguistic attributes of Tamil add layers of complexity to the caption generation process [6]. These complexities are reflected in the language's rich morphological structures and varied syntactic arrangements, posing a significant challenge for models predominantly trained on English-centric datasets and rules. As a result, these models often falter in generating fluent and contextually accurate captions in Tamil, highlighting the need for more linguistically and culturally adaptive systems. In conventional image captioning models, the significance of context is often under-played. Yet, the ability to contextualize is vital in crafting captions that transcend mere object enumeration and instead narrate a story that encapsulates the essence and interrelations of the elements within an image [7,8]. This aspect becomes even more critical in culturally diverse contexts, where images may bear specific connotations or symbolic meanings integral to a culture. Capturing this contextual dimension involves more than recognizing visual elements; it requires an understanding of the setting, the emotional tone, and the cultural nuances associated with the imagery. For instance, a depiction of a festival in Tamil culture might be imbued with unique cultural symbols and references. Neglecting these aspects could lead to captions that fail to capture the image's core elements and cultural significance. In response to these challenges, there is a pressing need for models that not only possess visual acuity but also exhibit a deep understanding of cultural and contextual subtleties. Such models should not only recognize and describe visual elements but also weave these elements into a narrative that is culturally resonant and contextually rich, especially for languages like Tamil that are rich in cultural and linguistic diversity. The pursuit of image captioning in low-resource languages like Tamil necessitates a nuanced understanding of the intersection between technology, language, and culture [9]. While deep learning has undeniably revolutionized the field, its reliance on large datasets poses a significant challenge in these contexts.