Tesseract’s accuracy for production depends entirely on document quality; it does not publish a universal accuracy figure. It excels with clean, high-resolution (300 DPI+), upright printed text on plain backgrounds. The engine struggles significantly with skewed or low-resolution images, uneven backgrounds, noise, tables, and handwriting, as it is designed for printed text. Independent benchmarks indicate that Tesseract performs less effectively than cloud-based alternatives, especially on noisy documents. To assess its suitability, users must test Tesseract on a sample of their own documents, with a predefined "go or no-go" threshold. This involves selecting 50 representative documents, manually labeling critical fields, and then evaluating Tesseract’s output. Measuring character error rate for free text and exact matches for numerical fields helps determine its accuracy. If Tesseract fails to meet the threshold, solutions include improved preprocessing, alternative OCR engines, or managed API services. Users should ensure control over scan quality, preprocess documents for common issues, and have a system for handling errors.