Hi, I just read your job posting and I think you need an effective image preprocessing pipeline in Python to optimize identification card images for OCR with Tesseract.
To achieve this, I propose a robust script using OpenCV and PIL for preprocessing tasks such as noise reduction using GaussianBlur or bilateral filtering, which ensures that text edges remain sharp while removing unnecessary distortions. For resizing and cropping, I will standardize dimensions by detecting regions of interest (ROI) and applying adaptive scaling, ensuring consistent input for Tesseract. Finally, I will implement adaptive histogram equalization (CLAHE) for contrast enhancement and brightness normalization, guaranteeing improved OCR accuracy even for poorly lit images.
This pipeline will be modular, allowing you to easily adjust parameters for different datasets, and well-documented for maintainability. With my experience working extensively with Python, OpenCV, PIL, and Tesseract OCR, I’m confident in delivering a solution that ensures high accuracy and efficiency in text extraction.
For a deeper understanding and success in this project, I would like you to explain in more detail what you are hoping for.