Lily Rojabiyati Mursari

Papers

Total Citations

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About

Lily Rojabiyati Mursari is a researcher whose work sits at the intersection of computer vision, artificial intelligence, and document analysis. Her primary research focus is on optical character recognition (OCR), particularly the enhancement of digital handwritten script recognition through image preprocessing techniques. Her most-cited paper, "The Effectiveness of Image Preprocessing on Digital Handwritten Scripts Recognition with The Implementation of OCR Tesseract" (2021, 22 citations), addresses a persistent challenge in the field: accurately extracting characters from handwritten images into machine-encoded text. By systematically evaluating how preprocessing steps—such as noise reduction, binarization, and normalization—improve OCR accuracy, Mursari has provided a practical framework for researchers and developers working with legacy documents, historical archives, or any handwritten data. Her work is notable for bridging the gap between theoretical image processing and real-world OCR deployment, offering clear guidelines that can be implemented in tools like Tesseract. While her citation count reflects a growing interest in this niche, her contributions are especially valuable for students and practitioners seeking to improve recognition systems for non-standard scripts. Mursari’s research underscores the importance of preprocessing as a critical, often underestimated step in building robust OCR pipelines.