Python Developer for OCR Accuracy Enhancement

This project aims to enhance the text recognition capabilities of an existing Python application ([login to view URL]), specifically targeting receipt images stored in the receipts directory. The primary goal is to preprocess images to maximize text recognition accuracy using Pytesseract and OpenCV. Key aspects of the project include: - Image Preprocessing: Implement various preprocessing techniques to improve OCR performance. This may include but is not limited to: - Converting images to grayscale or black and white to reduce complexity. - Applying filters to enhance text features. - Extracting the receipt's contour to eliminate background noise, ensuring the OCR focuses solely on the receipt text. - Modular Code Design: Each preprocessing step should be encapsulated within its independent function, promoting code modularity and readability. - Step-by-Step Visualization: After each preprocessing step, export an intermediate image. This will allow for a visual inspection of the preprocessing effects on the image, aiding in the fine-tuning of the preprocessing pipeline. - Library Use: While the primary libraries are OpenCV for image processing and Pytesseract for OCR, the use of additional support libraries is encouraged if they contribute to the project's goals. The project seeks not only to refine the accuracy of text recognition on receipt images but also to provide a clear, stepwise approach to image preprocessing, demonstrating the impact of each preprocessing technique on the OCR results. This structured approach will facilitate further adjustments and improvements to the preprocessing workflow.