OCR Fine-Tuning for Low-Quality Number Plate Recognition
Achieved a 50% increase in OCR accuracy for low-quality number plates
Usama N. is an AI Engineer with over four years of experience in machine learning, computer vision, and data annotation management. Proficient in Python, JavaScript, and C++, Usama builds machine learning models and computer vision applications using TensorFlow, PyTorch, Keras, Scikit-Learn, and OpenCV. He integrates generative AI tools such as the OpenAI Assistant API and Langchain, and employs MLOps practices with Docker, Kubernetes, and Git for scalable data management. Usama led a project that achieved a 50% increase in OCR accuracy for low-quality number plate recognition and orchestrated data preparation for Florence 2 models, enhancing model accuracy by 20% and data delivery efficiency by 30%. He has also designed data annotation pipelines for bike lane detection using CVAT. Holding a Master of Sciences in Data Science and a Bachelor of Sciences in Mathematics from the National University of Sciences and Technology, Usama is well-suited for roles focused on AI-driven solutions and model optimization, particularly in environments that value cross-functional collaboration and innovation.
Achieved a 50% increase in OCR accuracy for low-quality number plates
Designed and implemented data annotation pipelines using CVAT
Managed end-to-end data preparation and annotation processes
Orchestrated the end-to-end data preparation for fine-tuning Florence models, curating and annotating image datasets; boosted model accuracy by 20% and boosted data delivery efficiency by 30%.
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AI Engineer
Islamabad · 3-4 years experience
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