Enhancing the Accuracy of Optical Character Recognition (OCR) in Digitizing Old Academic Records: A Case Study of Ibrahim Badamasi Babangida University, Lapai (Lapai LGA, Niger State)
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Background of the Study
Optical Character Recognition (OCR) technology has become a vital tool for digitizing printed materials and transforming them into editable text. Ibrahim Badamasi Babangida University, Lapai, located in Lapai LGA, Niger State, faces challenges in digitizing its old academic records, which are often handwritten or printed in non-standard formats. Traditional OCR systems may struggle with poor-quality documents or non-standard fonts, leading to errors in the digitized output.
This study aims to enhance the accuracy of OCR systems in digitizing old academic records by exploring advanced OCR techniques and AI-based solutions, such as deep learning algorithms. The research will focus on improving the OCR system's ability to handle poor-quality and non-standard academic documents, ensuring that the records are accurately digitized for long-term storage and retrieval.
Statement of the Problem
The current OCR systems at Ibrahim Badamasi Babangida University, Lapai, are limited in their ability to accurately process old academic records that may be damaged, poorly printed, or handwritten. These limitations hinder the digitization process and reduce the overall quality of the converted records. There is a need for more advanced OCR techniques to improve accuracy in these situations.
Objectives of the Study
1. To evaluate the accuracy of existing OCR systems in digitizing old academic records at Ibrahim Badamasi Babangida University.
2. To enhance the accuracy of OCR systems by integrating AI-based techniques, such as deep learning and image preprocessing.
3. To assess the effectiveness of the improved OCR system in digitizing old academic records.
Research Questions
1. How accurate are current OCR systems in digitizing old academic records at Ibrahim Badamasi Babangida University?
2. What advanced OCR techniques can be used to improve the accuracy of digitization for old and non-standard academic records?
3. How does the enhanced OCR system compare to traditional methods in terms of accuracy and efficiency?
Research Hypotheses
1. AI-based enhancements to OCR systems will significantly improve the accuracy of digitizing old academic records.
2. The use of deep learning and image preprocessing techniques will reduce errors and increase the accuracy of OCR systems in handling non-standard records.
3. The improved OCR system will outperform traditional OCR systems in digitizing old academic records.
Significance of the Study
This study will contribute to improving the digitization of old academic records at Ibrahim Badamasi Babangida University, Lapai. The findings could help other universities facing similar challenges and lead to the development of more robust and accurate OCR systems for academic records digitization.
Scope and Limitations of the Study
The study will focus on enhancing OCR systems for digitizing old academic records at Ibrahim Badamasi Babangida University, Lapai. It will not address other forms of document digitization or broader AI applications in university record-keeping.
Definitions of Terms
• Optical Character Recognition (OCR): The technology used to convert different types of documents, such as scanned paper documents, PDFs, or images, into editable and searchable text.
• Deep Learning: A subset of machine learning that uses neural networks with many layers to process complex data, such as images and text.
• Image Preprocessing: Techniques used to improve the quality of images before applying OCR, such as noise reduction or image enhancement.
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