# Bangla Character Detection Using Enhanced YOLOv11 Models: A Deep Learning Approach
## 1 Introduction  
The Bangla characters, also known as the Bengali script (Bengali: Bangla bornomala), represent a writing system of profound cultural and historical importance. Spoken by over 278 million people, approximately 3.4% of the world's largest population, it is ranked seventh globally as of 2022 [1]. Bengali is the primary language in Bangladesh and is widely spoken in the Indian states of West Bengal, Assam, and Tripura [2]. The script traces its origins to the ancient Brahmic family of scripts, from which many South Asian writing systems, including Devanagari (used for Sanskrit and Hindi), evolved. Over the centuries, the Bengali script developed into a distinctive system, diverging from closely related scripts such as Oriya and Assamese through unique stylistic and structural adaptations. Due to its flexibility and expressive capacity, the Bengali script has historically been employed not only for writing the Bengali language but also for transcribing Sanskrit texts in the Bengal region. As one of the most widely used writing systems globally, it plays a vital role in regional communication, education, and the preservation of cultural heritage. The script comprises vowels, consonants, diacritical marks, conjunct consonants, numerals, and punctuation. It includes eleven vowel graphemes (sworoborno), which represent six basic vowel sounds and two diphthongs, used in both Bengali and Assamese. One of the key complexities of the script lies in conjunct consonants (juktakkhor), where clusters of up to four consonants merge to form visually complex shapes, significantly complicating segmentation and recognition. The Bengali numeral system is based on a positional base-10 structure and uses 10 distinct digits (0-9) for numeric representation. Accurate identification and classification of these diverse components require sophisticated recognition systems capable of handling the script's high visual density and structural complexity. Deep learning-based character recognition in Bengali presents unique challenges due to the script's structural complexity and distinctive visual features. While traditional optical character recognition (OCR) techniques have proven effective for scripts with simpler character sets, they often perform suboptimally when applied to the Bengali script. This limitation arises from the presence of visually similar graphemes with subtle structural differences, as well as complex conjunct consonants and overlapping diacritical marks. These features complicate segmentation and accurate classification in conventional OCR systems. The goal of this research is to develop a robust deep learning model that can effectively recognize and classify individual printed Bangla characters. Rather than relying on the conventional pipeline involving separate stages like segmentation, handcrafted feature extraction, and classification, this study adopts a more streamlined approach using a single-shot object detection method based on the YOLO (You Only Look Once) framework. We specifically focus on an improved version of YOLOv11, which is designed for high-speed inference and strong recognition accuracy. This upgraded architecture is tailored to handle the unique visual complexity of Bangla characters, many of which share subtle structural similarities. The system processes the entire image in one forward pass, enabling real-time recognition with lower computational cost. The architecture of our proposed model, as depicted in the diagram, is organized into three main sections: the backbone, neck, and detection head. At the core of the backbone is ResNet50, a deep residual network responsible for extracting rich feature representations from input images. This backbone is particularly effective in maintaining performance across deeper layers by mitigating the vanishing gradient problem through shortcut connections. Following the backbone, a Spatial Pyramid Pooling Fast (SPPF) module is used to enhance receptive fields and capture features at multiple scales.