Highly inconsistent OCR result for tesseract

Question

This is the original screenshot and I cropped the image into 4 parts and cleared the background of the image to the extent that I can possibly do but tesseract only detects the last column here and ignores the rest.

The output from the tesseract is shown as it is there are blank spaces which I remove while processing result

  Femme—Fatale.



  DaRkLoRdEIa
  aChineseN1gg4

  Noob_Diablo_

The output from the tesseract is shown as it is there are blank spaces which I remove while processing result

Kicked.

NosNoel
ChikiZD
Death_Eag|e_42

Chai—.

3579 10 1 7 148

2962 3 O 7 101

2214 2 2 7 99

2205 1 3 6 78

8212

7198

6307

5640

4884

15

40

40

6O

80

80

Am just dumping the output of

result = `pytesseract.image_to_string(Image.open("D:/newapproach/B&W"+str(i)+".jpg"),lang="New_Language")`

But I do not know how to proceed from here to get a consistent result.Is there anyway so that I can force the tesseract to recognize the text area and make it scan that.Because in trainer (SunnyPage), tesseract on default recognition scan it fails to recognize some areas but once I select the manually everything is detected and translated to text correctly

Code

Answer 1

Tried with the command line which gives us option to decide which psm value to be used.

Can you try with this:

pytesseract.image_to_string(image, config='--psm 6')

Tried with the image provided by you and below is the result:

Extracted Text Out of Image

The only problem I am facing is that my tesseract dictionary is interpreting "1" provided in your image to ""I" .

Below is the list of psm options available:

pagesegmode values are: 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.

Answer 2

I used this link

https://www.howtoforge.com/tutorial/tesseract-ocr-installation-and-usage-on-ubuntu-16-04/

Just use below commands that may increase accuracy upto 50%

sudo apt update

sudo apt install tesseract-ocr

sudo apt-get install tesseract-ocr-eng

sudo apt-get install tesseract-ocr-all

sudo apt install imagemagick

convert -h

tesseract [image_path] [file_name]

convert -resize 150% [input_file_path] [output_file_path]

convert [input_file_path] -type Grayscale [output_file_path]

tesseract [image_path] [file_name]

It will only show bold letters

Thanks

Answer 3

My suggestion is to perform OCR on the full image.

I have preprocessed the image to get a grayscale image.

import cv2
image_obj = cv2.imread('1D4bB.jpg')
gray = cv2.cvtColor(image_obj, cv2.COLOR_BGR2GRAY)
cv2.imwrite("gray.png", gray)

I have run the tesseract on the image from the terminal and the accuracy also seems to be over 90% in this case.

tesseract gray.png out

3579 10 1 7 148
3142 9 o 5 10
2962 3 o 7 101
2214 2 2 7 99
2205 1 3 6 78
Score Kills Assists Deaths Connection
8212 15 1 4 4o
7198 7 3 6 40
6307 6 1 5 60
5640 2 3 6 80
4884 1 1 5 so

Below are few suggestions -

1. Do not use image_to_string method directly as it converts the image to bmp and saves it in 72 dpi.
2. If you want to use image_to_string then override it to save the image in 300 dpi.
3. You can use run_tesseract method and then read the output file.

Image on which I ran OCR.

Another approach for this problem can be to crop the digits and deep to a neural network for prediction.

Answer 4

I think that you have to preprocess the image first, the changes that works for me are: Supposing

import PIL
img= PIL.Image.open("yourimg.png")

• Make the image bigger, I usually double the image size.

  img.resize(img.size[0]*2, img.size[1]*2)

• Grayscale the image

  img.convert('LA')

• Make the characters bolder, you can see one approach here: https://blog.c22.cc/2010/10/12/python-ocr-or-how-to-break-captchas/ but that approach is fairly slow, if you use it, I would suggest to use another approach

• Select, invert selection, fill with black, white using gimpfu

  image = pdb.gimp_file_load(file, file) layer = pdb.gimp_image_get_active_layer(image) REPLACE= 2 pdb.gimp_by_color_select(layer,"#000000",20,REPLACE,0,0,0,0) pdb.gimp_context_set_foreground((0,0,0)) pdb.gimp_edit_fill(layer,0) pdb.gimp_context_set_foreground((255,255,255)) pdb.gimp_edit_fill(layer,0)

  pdb.gimp_selection_invert(image) pdb.gimp_context_set_foreground((0,0,0))

Answer 5

fn = 'image.png'
img = cv2.imread(fn, 0)
img = cv2.bilateralFilter(img, 20, 25, 25)
ret, th = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
# Image.fromarray(th)
print(pytesseract.image_to_string(th, lang='eng'))