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image-table-ocr/table_ocr/ocr_image/__main__.py

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Fix bugs and improve accuracy Files in the ocr_to_csv module need to be named in a certain way. Specify that and fix a bug, we need to have them sorted lexicographically. Don't dilate the characters in a cell in order to make a contiguous set of pixels that we can find a contour around. The problem with that is that you sometimes dilate too far and hit an image boundary and can't erode back in. If a cell wall border was remaining between the text and the image boundary, well now you're keeping that border line in the image. (Unless you remove it some other way. So that might be a valid option in the future.) The method we're using now instead is to group all contours together and create a bounding box around all of them. The problem with that is if there is any noise at all outside the text, we're grabbing it. Before, we were dilating and taking the largest contour, so we weren't including that noise. And we can't get rid of the noise with opening morph because it's sometimes pretty big noise and opening any bigger distorts the text so much that we lose accuracy in finding those boundaries. Also adds a shell script to simplify the plumbing of all these modules.
4 years ago
import argparse
Add ocr_image module
4 years ago
Make ocr_image return/print path of text file Move the main function to the __init__ file so it can be imported by other code. Modify it so that it returns the path to the file that contains the OCR text so that calling code can keep find the results.
4 years ago
from table_ocr.ocr_image import main
Add ocr_image module
4 years ago
Fix bugs and improve accuracy Files in the ocr_to_csv module need to be named in a certain way. Specify that and fix a bug, we need to have them sorted lexicographically. Don't dilate the characters in a cell in order to make a contiguous set of pixels that we can find a contour around. The problem with that is that you sometimes dilate too far and hit an image boundary and can't erode back in. If a cell wall border was remaining between the text and the image boundary, well now you're keeping that border line in the image. (Unless you remove it some other way. So that might be a valid option in the future.) The method we're using now instead is to group all contours together and create a bounding box around all of them. The problem with that is if there is any noise at all outside the text, we're grabbing it. Before, we were dilating and taking the largest contour, so we weren't including that noise. And we can't get rid of the noise with opening morph because it's sometimes pretty big noise and opening any bigger distorts the text so much that we lose accuracy in finding those boundaries. Also adds a shell script to simplify the plumbing of all these modules.
4 years ago
description="""Takes a single argument that is the image to OCR.
Remaining arguments are passed directly to Tesseract.
Attempts to make OCR more accurate by performing some modifications on the image.
Saves the modified image and the OCR text in an `ocr_data` directory.
Filenames are of the format for training with tesstrain."""
parser = argparse.ArgumentParser(description=description)
parser.add_argument("image", help="filepath of image to perform OCR")
Make ocr_image return/print path of text file Move the main function to the __init__ file so it can be imported by other code. Modify it so that it returns the path to the file that contains the OCR text so that calling code can keep find the results.
4 years ago
args, tess_args = parser.parse_known_args()
print(main(args.image, tess_args))