Companion tutorial blog post can be found here

Basic functions for different preprocessing methods

• grayscaling
• thresholding
• dilating
• eroding
• opening
• canny edge detection
• noise removal
• deskwing
• template matching.

Different methods can come in handy with different kinds of images.

While running and image through the tesseract OCR engine, pytesseract allows you to get bounding box imformation

• on a character level
• on a word level
• based on a regex template

We will see how to obtain all of them.

There are several ways a page of text can be analysed. The tesseract api provides several page segmentation modes if you want to run OCR on only a small region or in different orientations, etc.

Here's a list of the supported page segmentation modes by tesseract -

0 Orientation and script detection (OSD) only.
1 Automatic page segmentation with OSD.
2 Automatic page segmentation, but no OSD, or OCR.
3 Fully automatic page segmentation, but no OSD. (Default)
4 Assume a single column of text of variable sizes.
5 Assume a single uniform block of vertically aligned text.
6 Assume a single uniform block of text.
7 Treat the image as a single text line.
8 Treat the image as a single word.
9 Treat the image as a single word in a circle.
10 Treat the image as a single character.
11 Sparse text. Find as much text as possible in no particular order.
12 Sparse text with OSD.
13 Raw line. Treat the image as a single text line, bypassing hacks that are Tesseract-specific.

To change your page segmentation mode, change the --psm argument in your custom config string to any of the above mentioned mode codes.

By making minor changes in the config file you can

• specify language
• detect only digits
• whitelist characters
• blacklist characters
• work with multiple languages