I think you have to fiddle with the internal settings of the OCR package you are using to get good results. For tesseract and pytesseract, there is a whole arti

I think you have to fiddle with the internal settings of the OCR package you are using to get good results. For tesseract and pytesseract, there is a whole article on improving the quality: https://github.com/tesseract-ocr/tesseract/wiki/ImproveQuality https://github.com/tesseract-ocr/tesseract/wiki/ImproveQuali... My guess is Apple trained on their own massive dataset and better architectures that make their systems better than off the shelf ocr. I am working in this area and one way I see good improvement is training an object detector to first detect words in an image, then you can pass that through tesseract/OCR software. Besides that, finetuning tesseract on data you want strong performance would be the next best alternative. Here is a cool article from dropbox how they engineering their OCR system: https://blogs.dropbox.com/tech/2017/04/creating-a-modern-ocr-pipeline-using-computer-vision-and-deep-learning/ https://blogs.dropbox.com/tech/2017/04/creating-a-modern-ocr... This link below is a really cool project that showcases what building your own OCR engine would look like! https://github.com/awslabs/handwritten-text-recognition-for-apache-mxnet https://github.com/awslabs/handwritten-text-recognition-for-...