image processing to improve tesseract OCR accuracy

Question

I\'ve been using tesseract to convert documents into text. The quality of the documents ranges wildly, and I\'m looking for tips on what sort of image processing might impr

Answer 1

Reading text from image documents using any OCR engine have many issues in order get good accuracy. There is no fixed solution to all the cases but here are a few things which should be considered to improve OCR results.

1) Presence of noise due to poor image quality / unwanted elements/blobs in the background region. This requires some pre-processing operations like noise removal which can be easily done using gaussian filter or normal median filter methods. These are also available in OpenCV.

2) Wrong orientation of image: Because of wrong orientation OCR engine fails to segment the lines and words in image correctly which gives the worst accuracy.

3) Presence of lines: While doing word or line segmentation OCR engine sometimes also tries to merge the words and lines together and thus processing wrong content and hence giving wrong results. There are other issues also but these are the basic ones.

This post OCR application is an example case where some image pre-preocessing and post processing on OCR result can be applied to get better OCR accuracy.