![]() If an image fails QC or is rejected by the OCR engine, it is "repaired" by hand using a custom. I use commercial Windows product from Recogniform to process and clean up the images prior to OCR in a batch mode using scripts adjusted for various kinds of images. (I'm not a big fan of OCR but it has its place) You will find that the OCR results rise quickly at first and then level off sooner than you expected. If you are processing several hundred thousand images a month, then I would suggest you divide up the process into smaller workflow step and tweak each one until your cost per image gets as close to zero as you can. At least this let's you put everything you need under one application for a low cost. Of course, you are on your own if the results are not improving. For small batches and tight budgets, I agree with the previous posters that projects like Aforge, Paint.NET, and other open source computer vision libraries will do the trick. Managed code and imaging tool kits will work but it's not always the best solution if you haved several million images to process. ![]() Depends on the number and quality of the original images.
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