OCR in 2026: Why Tesseract Still Beats Most Commercial APIs

Optical Character Recognition has been a solved problem for about thirty years, except that it has not. Clean printed text on a white background? Any OCR engine handles that with near-perfect accuracy. A photo of a receipt taken at an angle in bad lighting with creases and shadows? That is where things get interesting, and where the gap between good and great OCR still matters. At its core, OCR is a pipeline with distinct stages. Understanding each stage helps explain why some engines succeed where others fail. The first stage is binarization: converting the image to pure black and white. This sounds trivial, but it is arguably the most important step. Simple global thresholding (everything above a certain brightness becomes white, everything below becomes black) fails badly on images with uneven lighting. Adaptive thresholding, where the threshold varies across different regions of the image, handles real-world conditions far better. Sauvola's method and Niblack's method are two commo