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Improving Accuracy of Text Extraction with Simple Techniques
Latest   Machine Learning

Improving Accuracy of Text Extraction with Simple Techniques

Last Updated on July 25, 2023 by Editorial Team

Author(s): Chinmay Bhalerao

Originally published on Towards AI.

understanding data and problem statements to make predictions better

Photo by Clément Hélardot on Unsplash

Text extraction has a lot of applications in extracting text from images, scanned documents, and pdfs. There are many modules that we can use with python to extract texts, like pytesseract, easyOCR, Keras OCR, and many… But many times, we don't get output as we want due to the constraints of these libraries. We can use many simple techniques with libraries to make our output better. Let's see how we can improve output in this blog.

Part1:Simple text extraction

Importing basic libraries

import cv2
import pytesseract
import pandas as pd

We will use pytesseract for text extraction because it is most commonly used for text extraction applications.

new_dict = []
#read image
img = cv2.imread(r'Raw image address')
cv2.imshow('Test image', img)
# cv2.imwrite(filename, edges)
cv2.waitKey(0)
cv2.destroyAllWindows()

Now we will read the image.

Image for text extraction [Source: Image by author]

I purposefully took the harder image with a lot of noise because we wanted to see the performance of simple text extraction on the complex images.

Let's start text extraction.

# configurations
config = ('-l eng --oem 1 --psm 8')
# pytessercat
pytesseract.pytesseract.tesseract_cmd = 'C:/Program Files (x86)/Tesseract-OCR/tesseract.exe'
text = pytesseract.image_to_string(img, config=config)

If you downloaded pytesseract, then the above one should be the location to store it. It will help you execute and use all language models effectively.

In the text variable, we are storing all texts that are extracted by pytesseract model.

The psm mode is very important in extraction. psm stands for “page segmentation mode” how to scan an image to extract text is done by psm mode. If you want to read more on psm then go through the below blog.

Tesseract Page Segmentation Modes (PSMs) Explained: How to Improve Your OCR Accuracy …

Most introductions to Tesseract tutorials will provide you with instructions to install and configure Tesseract on your…

pyimagesearch.com

After using psm mode 8, we got the following output

Image by author

If you see this, then it is irrelevant and does not give any information from a text extraction perspective. Let's change psm mode to 4.

Image by author

This output is better than psm 8. So we will take this mode to refine our text extraction model.

part2:Imroving performance

Many times there is a case where we get output from text extraction, but it is somewhat distorted than the original word.

Ex. the original word is KITCHEN, and the model is giving kitc, kitche, tchen, KITC, kitchen like any combination of words. So for this possibility, we will use one technique with one library.

the library difflib is used to calculate the difference between the original and the predicted word. Working on the difflib is very simple. You have to provide a tentative dictionary of words that may come into your images for extraction. Then this library asks for the threshold to match. Any possibility which is matching with our words in the dictionary with the given threshold will get replaced with the original word from the dictionary.

for i in text:
new_dict.append(i)
new = []
for i in text[0]:
new.append(i)

importing and working with Difflib

import difflib
#close_matches = []
possibilities = ["Drawing","Bedroom", "Hall", "Kitchen", "Balcony", "Dining","Sitout","SITOUT","HALL","KITCHEN","BEDROOM","HALL","LOBBY","TOILET"]
n = 3
cutoff = 0.3
emptyDict = {}

for w in new:
if (difflib.get_close_matches(w, possibilities, n, cutoff)):
#print (w)
i = new.index(w)
emptyDict[w] = new[i+1]
else:
continue

If you use the above code and print emptydict then the following is the output.

Image by author

It is much more accurate and usable than the output that we previously had without any preprocessing.

What if we want a specific special character and want to eliminate any other present in extraction?

We have a solution for that. A RegEx, or Regular Expression, is a library that we can use to solve this problem.

 Final_df = pd.concat(Finaldf, ignore_index=True)

Final_df = Final_df.replace('=', '', regex=True)
Final_df = Final_df.replace('x', '', regex=True)
Final_df = Final_df.replace('X', '', regex=True)
Final_df = Final_df.replace('{', '', regex=True)
Final_df = Final_df.replace('\', '', regex=True)
Final_df = Final_df.replace('
°', '', regex=True)

After concatenating every extraction in one dataframe, we can use the above code to replace desired signs.

Image by author

Better than the previous one!!

The better option for such cases is to change the library and go for higher-level libraries like EasyOCR, Google vision, etc. but these small changes can be useful when we are doing any kind of text extraction.

Above mentioned technique you can use when you are sure about data keywords that we want to extract, and you can update your dictionary of mapping at any stage for new words.

Other ways to improve the accuracy of the model:

1) Select only that language which are present in your source document

This reduces wrong interpretations of characters and patterns and helps to reduce noise. Like in Tesseract, you can select a language package.

2) Text rotations

Most OCR engines work well with horizontal and vertical alignment. It gets harder when there is skewness or angled rotation in the text. Some OCR engines have PSM (page segmentation mode), where we can select the orientation of scanning.

3) Lighting of image

Brightness and contrast are the two things that enhance the readability and explicitness of features for text extraction. there are many apps that can help to adjust the contrast and brightness of an image. The proper lighting condition can improve the results of OCR.

4) Image extension

Compressions, resizing, and other image manipulations tend to lose image information. .JPG format tends to lose more data after such operations where. PNG and . TIFF doesn’t lose data to such an extent. Choosing image format wisely can improve OCR’s efficiency. Below mentioned python code can be useful to convert images into desired DPI.

from PIL import Image
img = Image.open("IMAGE.png")
img.save("Converted_IMAGE-600.png", dpi=(300,300))

5) Image quality

Dots per inch or DPI is the main factor when considering the quality of the image. A resolution of less than 300 DPI can make the image unclear. There are many DPI conversion tools online that can get you the required DPI. So try to use a higher DPI image.

For more understanding of concepts like DPI, Resolution, PNG, JPG & other image formats, PPI, Lossless formats, etc.

U+007C Useful materials U+007C

  1. I found one pretty good series to understand OCR by Python Tutorials for Digital Humanities . This is a link for the video series on OCR.
  2. One interesting GitHub account I found was of Kba for OCR. Here is the link: kba /awesome OCR . You will get to know many things as well as interesting projects with source code from this account.
  3. I found one excellent book “OCR with Tesseract, OpenCV, and Python” by Dr.Adrian Rosebrock .link for the book: Book link

So these are all things that can help you to solve your text extraction-related problem statements at basic levels.

4. A project for “OCR models reading Captchas” you can refer to this video.

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Signing off,

Chinmay

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