This research aims to explain the requirements of constructing software intended for the teaching department of ACLC College of Butuan (ACB) to optimize the course selection process, lessen the academic advisor’s time and effort, and give an accurate result in academic advising. Course Evaluation Generator (CEG) solved the issues encountered in course advising: incorrectness in crediting courses, manual automation in generating courses to offer, and manual evaluation of the remaining courses of a program. The CEG used Tesseract Optical Character Recognition (OCR) to read text from the academic transcript (TOR) by transforming the text into a digital image. To test the prototype constantly, this paper used the RAD approach in the development. The system was tested, deployed, and evaluated by the user respondents. Using the ISO 25010, the weighted mean for accuracy is 3.14 that user-respondents have strongly agreed to the correctness of generating courses to offer for a student. Even though the system itself has met the required functions and features using the Tesseract, the respondents and researchers suggest that more advanced machine learning should be integrated to enhance this study for future research.

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