ScoreLens: Automated Student Result Processing and Analysis from Academic Gazette PDF

Abstract: The management and analysis of student academic results in diploma education systems often rely on manually processing gazette PDF documents, which is time-consuming and susceptible to human error. This paper presents the design and implementation of an automated system for extracting, structuring, and analyzing student results specifically based on MSBTE (Maharashtra State Board of Technical Education) standard formats and academic requirements. The proposed system efficiently processes structured PDF gazette files, extracts relevant student information, and converts it into a well-organized format suitable for analysis. The system utilizes PDF parsing techniques through PyMuPDF to accurately extract data from MSBTE-formatted documents, followed by data structuring and processing using Python libraries such as Pandas and OpenPyXL. It performs comprehensive result analysis, including subject-wise performance evaluation, overall result computation, and structured report generation in Excel format. By adhering to MSBTE standards, the system ensures compatibility, consistency, and reliability in handling academic records. Unlike existing approaches that rely heavily on OCR and machine learning techniques for unstructured data processing , the proposed system focuses on a domain-specific, rule-based approach optimized for structured academic documents. Experimental results demonstrate improved efficiency, reduced manual effort, and high accuracy in data extraction and analysis. The system provides a scalable solution for academic institutions and can be extended in the future to support unstructured formats using advanced techniques such as OCR and natural language processing.

Keywords: Student Performance Analysis, Document Parsing, PDF Data Extraction, Educational Data Mining, Automated Academic Systems, Data Structuring, Result Analytics, Python-Based System, MSBTE Gazette Processing, Information Extraction.