Turning Turnitin Highlights into Structured Data with Report-Aware HSV Segmentation and GPU-Enabled OCR
Keywords:
Turnitin, HSV color segmentation, optical character recognition, document analysis, similarity analyticsAbstract
Turnitin similarity reports contain rich spatial, color, and textual information, yet highlighted passages are usually inspected visually rather than converted into structured data. This study develops a report-aware pipeline that transforms color-coded Turnitin highlights into analyzable records. The method combines PDF rendering, page-type triage, hue–saturation–value (HSV) segmentation, morphological processing, connected-component analysis, geometric filtering, and GPU-enabled optical character recognition (OCR), with PDF text-layer inspection as supporting information. The evaluation corpus comprised 106 reports and 3,057 pages. Page triage identified 1,315 highlight-bearing pages and routed 56.98% of pages away from detailed processing. HSV segmentation produced eight color classes and 11,471 pre-filter components. After filtering and text extraction, 7,530 structured records were retained; 76.75% satisfied the predefined good-readability criterion, 20.04% were very short fragments, and 3.21% were noisy, garbled, or empty. Document workload was strongly right-skewed. The results establish systems-level feasibility for auditable extraction without treating textual similarity as automatic evidence of plagiarism or the readability indicator as OCR accuracy.
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