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. Check it out here
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 language
