## 1 Introduction  
Image captioning presents a challenging task in the realms of computer vision and natural language processing, requiring the generation of textual descriptions for given input images [1]. In the field of linguistics, particularly in translation and interpreting, this approach can provide accurate visual representations, precise technical terms, images, meanings, descriptions, and contexts. Translators and interpreters can rely on it to quickly access data about specific terms. In recent years, deep learning-based approaches have emerged as a promising solution to the image captioning problem, harnessing the capabilities of deep neural networks to learn intricate mappings between images and text [2]. Typically, an encoder-decoder structure is employed for this task, with neural networks serving as encoders to process the input image and extract high-level feature representations. Furthermore, neural networks are commonly utilized as decoders, generating a sequence of words describing the image based on the features extracted by the encoder. The synergy between these two networks has significantly enhanced the performance of image captioning systems, approaching human-level performance on certain benchmark datasets [3]. An artificial intelligence (AI) tool capable of automatically identifying and describing jewelry can emulate human intuitive behavior, fostering increased connections between individuals from diverse cultures and customs. This includes overcoming language and cultural barriers by providing accurate and detailed descriptions of jewelry in various languages. Such an AI tool can encourage curiosity and cultural exchange by offering information about the history, symbolism, and craftsmanship of jewelry, sparking meaningful conversations and dialogues between individuals of different backgrounds. Additionally, it can promote appreciation and respect for diversity, fostering tolerance, inclusivity, and harmony among individuals and communities with different traditions and customs. Moreover, automatic jewelry recognition has practical applications in cultural commerce and tourism, assisting buyers and collectors in making informed decisions and engaging in fair trade of cultural jewelry, as well as attracting tourists interested in exploring and acquiring traditional jewelry during their travels. In this study, various image captioning encoder-decoder structures are assessed using a dataset comprising images of jewelry accessories. The dataset was created by collecting images of diverse accessories from two well-known online jewelry stores in the city of Cordoba, Spain. Cordoba is celebrated for its artisanal craftsmanship, particularly in the creation of high-quality silver jewelry. The city hosts notable jewelry fairs and events that exhibit distinctive designs, attracting both local and international buyers. The utility of AI in linguistics becomes evident in unraveling the intricate linguistic landscape of jewelry terminology. AI-powered linguistic analysis can efficiently process vast datasets of historical documents, dowry letters, and cultural references related to jewelry. It helps in identifying patterns, tracing linguistic evolution, and discerning the cultural contexts from which specific terms and expressions emerged. Given the limited application of image captioning within this specific field of study, the task is intricate and notably captivating. The ultimate goal of the project is to generate detailed captions for accessories featured in the input images, highlighting characteristics such as their type (earrings, necklace, bracelet, etc. ), material, color, and the specific jewelry they showcase. The images encompass individuals wearing the accessories, suggesting the potential application of the system to automatically generate descriptions for accessories worn by individuals in images sourced from fashion magazines, jewelry catalogs, and jewelry trade shows, among other sources.