Performing OCR on non-document images involves extracting text from scenes, signs, or objects where traditional OCR might struggle. Preprocess the image using OpenCV to improve text visibility by resizing, binarizing, or enhancing contrast.
Use OCR tools like Tesseract with fine-tuned configurations for non-document settings. For example, Tesseract’s -psm parameter can be adjusted for specific layouts. Deep learning-based OCR models, such as EasyOCR or Google’s Vision API, often yield better results for complex scenarios.
Postprocess the extracted text to correct errors and improve accuracy. Combining OCR with object detection models can also help localize text regions in cluttered images.