Delays, inconsistencies in evaluation, and human error are common problems associated with checking by hand programming assignments. This study thus presented an attempt in developing Handwritten Terminal-Based Program Compiler and Checker that aims at automation in assessing handwritten Java and C++ code using Optical Character Recognition (OCR) and terminal-based execution. Using a camera module and a mechanical paper feeder, the Raspberry Pi-based device scanned handwritten input, transformed it into digital text, identified logical and syntactic mistakes, and used black-box testing to verify that the output was correct. The system also included an originality check to identify potential code plagiarism and offer a structured analysis of the findings. Iterative design, testing, and project refinement were highlighted by the Agile development methodology. The OCR portion, code syntax checker, logic validator, and hardware integration were among the functional modules that went through extensive testing. The outcome demonstrated that the software modules for OCR, syntax validation, and imaging were seamlessly coordinated with the hardware components of the microcontroller, paper handling system, and imaging device. The evaluation done based on the ISO 25010 software quality model extracted an overall weighted mean of 4.39, corresponding to a rating of “Excellent.” Whereas data quality assessment using the ISO 25012 criteria yielded an overall weighted mean of 4.15, indicating “Very Satisfactory.” Instructors and students alike confirmed that the system was user-friendly, reduced grading times, and produced precise and consistent results. In sum, this represented an efficient, scalable, and automated means to assess handwritten scripts within academia. By minimizing the need for manual verification, it provided greater consistency in grading, relieved some of the instructor’s workload, and allowed for faster feedback to students. In turn, this shows potential to improve learning outcomes by identifying and correcting programming errors in a timely fashion. Reduced human error and faster testing cycles differentiate the system from existing automated grading tools. The successful utilization of the system demonstrates its genuine practicability as a solution for advancing assessment processes in programming education.

Keywords: Handwritten Code, OCR, Compiler, Program Checker, Automated Grading, Programming Assessment