Prashant C.

If your AI model performance depends on data quality, I can help. I work on building and validating high-quality training datasets across computer vision, OCR, and healthcare imaging use cases. My focus is not just annotation, but ensuring consistency, accuracy, and reliability through structured workflows and quality control systems. With 3+ years of experience, I’ve contributed to projects involving OCR, ANPR, facial recognition, and high-precision medical imaging, where even small annotation errors can impact model outcomes. My approach includes: * Clear guideline interpretation and execution * Consistent annotation across large datasets * Multi-level quality checks to reduce errors and rework * Handling edge cases to improve dataset reliability What I can support beyond annotation: * Annotation guideline understanding and refinement * Quality validation (Correct / Wrong / Missed analysis) * Identifying inconsistencies in datasets * Supporting dataset preparation for model training If you’re looking for someone who understands both annotation and data quality impact on models, I can support your project.