This research addressed the challenge encountered by beginner guitarists while using traditional music notation by proposing an approach to convert the traditional notation into an easier guitar tablature notation, which makes reading and exploring advanced musical compositions easy for them. Despite various resources being available for both notations, lack of tools for automated conversion of traditional notation into tablature necessitated the development of a tool for mapping finger placements directly onto the guitar fretboard. The proposed approach utilizes optical music recognition (OMR) to automate the conversion of music sheet images into guitar tablature. The implementation involved employing image processing with OpenCV, for grayscale conversion, binarization, edge detection, and staff line detection in music sheet images uploaded by the user. This is followed by the integration of the Audiveris OMR framework for accurate musical symbol recognition. A rule-based system was developed for interpreting note pitches and durations, which created a mapping of these notes to corresponding guitar tablature positions which are also optimized for playability. The generated tablature is in ASCII format, with export options available in PDF and JPG. Results showed that the system effectively processed the music sheet images and recognized musical symbols, which ensured seamless conversion to tablature, enhancing accessibility for beginner guitarists. The user-friendly interface developed with PyQt5 allowed easy interaction, making the tool suitable for users with minimal technical expertise. Future developments could focus on expanding the system's capabilities to detect complex musical notations, support for additional string instruments, and features for real-time audio input and cloud collaboration, promoting community engagement among musicians.