I've got this picture (preprocessed image) from which I want to extract the numeric values of each line. I'm using pytesseract but it doesnt show any results for this image.
I've tried several config options from other questions like "--psm 13 --oem 3" or whitelisting numbers but nothing yields results.
As a result I usually get just one or two characters or ~5 dots/dashes but nothing even remotly resembling the size of my input.
I hope someone can help me cheers in advance for your time.
pytesseract version: 0.3.8
tesseract version: 5.0.0-alpha.20210506
You must think to use --psm 4, it's more appropriate for your image. I also recommend to rethink about the image pre-process. Tesseract is not perfect and it requires good image as input to work well.
import cv2 as cv
import pytesseract as tsr
img = cv.imread('41DAx.jpg')
img = cv.cvtColor(img, cv.COLOR_BGR2RGB)
config = '--psm 4 -c tessedit_char_whitelist=0123456789,'
text = tsr.image_to_string(img, config=config)
print(text)
The above code was not able to well detect all digts in the image, but almost of them. Maybe with a bit of image pre-processing, you can reach your objective.
Related
I'm trying to take an image that's just a number 1-10 in a bubble font and use pytesseract to get the number.
Picture in question:
Here is an article that makes this process seem straightforward:
https://towardsdatascience.com/read-text-from-image-with-one-line-of-python-code-c22ede074cac
lives = pyautogui.locateOnScreen('pics/lives.png', confidence = 0.9)
ss = pyautogui.screenshot(region=(lives[0]+lives[2],lives[1],lives[2]-6,lives[3]))
ss.save('pics/tempLives.png')
img = cv2.imread('pics/tempLives.png')
cv2.imwrite('pics/testPic.png',img)
test = pytesseract.image_to_string(img)
print(test)
I know 'img' is the same as the image provided because I've used ss.save cv2.imwrite to see it.
I suppose my question is why it works so well in the article yet I cannot manage to get anything to print? I suppose the bubble font makes it trickier, but in the article those blue parentheses were read easily, so that makes me think this font wouldn't be too hard. Thanks for any help!
There are many cases when PyTesseract fails to recognize the text, and in some cases we have to give it some hints.
In the specific image you have posted, we better add config=" --psm 6" argument.
According to Tesseract documentation regarding PSM:
6 Assume a single uniform block of text.
Here is a code sample that manages to identify the text from the posted image:
import cv2
import pytesseract
#pytesseract.pytesseract.tesseract_cmd = r'C:\Program Files\Tesseract-OCR\tesseract.exe' # May be required when using Windows
img = cv2.imread('pics/testPic.png') # Reading the input image (the PNG image from the posted question).
text = pytesseract.image_to_string(img, config=" --psm 6") # Execute PyTesseract OCR with "PSM 6" configuration (Assume a single uniform block of text)
print(text) # Prints the text (prints 10).
Note:
The OCR is not always working, and there are many techniques to improve the OCR accuracy.
I have attached a very simple text image that I want text from. It is white with a black background. To the naked eye it seems absolutely legible but apparently to tesseract it is a rubbish. I have tried changing the oem and psm parameters but nothing seems to work. Please note that this works for other images but for this one.
Please try running it on your machine and see if it works. Or else I might have to change my ocr engine altogether.
Note: It was working earlier until I tried to add black pixels around the image to help the extraction process. Also I don't think that tesseract was trained on black text on a white background. It should be able to do this too. Also if this was true why does it work for other text images that have the same format as this one
Edit: Miraculously I tried running the script again and this time it was able to extract Chand properly but failed in the below mentioned case. Also please look at the parameters I have used. I have read the documentation and I feel this would be the right choice. I have added the image for your reference. It is not about just this image. Why is tesseract failing for such simple use cases?
To find the desired result, you need to know the followings:
Page-segmentation-modes
Suggested Image processing methods
The input images are boldly written, we need to shrink the bold font and then assume the output as a single uniform block of text.
To shrink the images we could use erosion
Result will be:
Erode
Result
CHAND
BAKLIWAL
Code:
# Load the library
import cv2
import pytesseract
# Initialize the list
img_lst = ["lKpdZ.png", "ZbDao.png"]
# For each image name in the list
for name in img_lst:
# Load the image
img = cv2.imread(name)
# Convert to gry-scale
gry = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Erode the image
erd = cv2.erode(gry, None, iterations=2)
# OCR with assuming the image as a single uniform block of text
txt = pytesseract.image_to_string(erd, config="--psm 6")
print(txt)
I want to be able to recognize digits from images. So I have been playing around with tesseract and python. I looked into how to prepare the image and tried running tesseract on it and I must say I am pretty disappointed by how badly my digits are recognized. I have tried to prepare my images with OpenCV and thought I did a pretty good job (see examples below) but tesseract has a lot of errors when trying to identify my images. Am I expecting too much here? But when I look at these example images I think that tesseract should easily be able to identify these digits without any problems. I am wondering if the accuracy is not there yet or if somehow my configuration is not optimal. Any help or direction would be gladly appreciated.
Things I tried to improve the digit recognition: (nothing seemed to improved the results significantly)
limit characters: config = "--psm 13 --oem 3 -c tessedit_char_whitelist=0123456789"
Upscale images
add a white border around the image to give the letters more space, as I have read that this improves the recognition process
Threshold image to only have black and white pixels
Examples:
Image 1:
Tesseract recognized: 72
Image 2:
Tesseract recognized: 0
EDIT:
Image 3:
https://ibb.co/1qVtRYL
Tesseract recognized: 1723
I'm not sure what's going wrong for you. I downloaded those images and tesseract interprets them just fine for me. What version of tesseract are you using (I'm using 5.0)?
781429
209441
import pytesseract
import cv2
import numpy as np
from PIL import Image
# set path
pytesseract.pytesseract.tesseract_cmd = r'C:\\Users\\ichu\\AppData\\Local\\Programs\\Tesseract-OCR\\tesseract.exe';
# load images
first = cv2.imread("first_text.png");
second = cv2.imread("second_text.png");
images = [first, second];
# convert to pillow
pimgs = [];
for img in images:
rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB);
pimgs.append(Image.fromarray(rgb));
# do text
for img in pimgs:
text = pytesseract.image_to_string(img, config='--psm 10 --oem 3 -c tessedit_char_whitelist=0123456789');
print(text[:-2]); # drops newline + end char
I installed pytesseract via pip and its result is terrible.
As I searched for it, I think I need to give it more data
but I can't find where to put tessedata(traineddata)
since there is no directory like ProgramFile\Tesseract-OCR using Mac.
There is no problem with images' resolution, font or size.
Image whose result is 'ecient Sh Abu'
Because large and clear test images work fine, I think it is a problem about lack of data.
But any other possible solution is welcomed as long as it can read text with Python.
Please help me..
I installed pytesseract via pip and its result is terrible.
Sometimes you need to apply preprocessing to the input image to get accurate results.
Because large and clear test images work fine, I think it is a problem about lack of data. But any other possible solution is welcomed as long as it can read text with Python.
You could say lack of data is a problem. I think you'll find morphological-transformations useful.
For instance if we apply close operation, the result will be:
The image looks similar to the original posted image. However there are slight changes in the output images (i.e. Grammar word is slightly different from the original image)
Now if we read the output image:
English
Grammar Practice
ter K-SAT (1-10)
Code:
import cv2
from pytesseract import image_to_string
img = cv2.imread("6Celp.jpg")
gry = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
opn = cv2.morphologyEx(gry, cv2.MORPH_OPEN, None)
txt = image_to_string(opn)
txt = txt.split("\n")
for i in txt:
i = i.strip()
if i != '' and len(i) > 3:
print(i)
I'm trying to read different cropped images from a big file and I manage to read most of them but there are some of them which return an empty string when I try to read them with tesseract.
The code is just this line:
pytesseract.image_to_string(cv2.imread("img.png"), lang="eng")
Is there anything I can try to be able to read these kind of images?
Thanks in advance
Edit:
Thresholding the image before passing it to pytesseract increases the accuracy.
import cv2
import numpy as np
# Grayscale image
img = Image.open('num.png').convert('L')
ret,img = cv2.threshold(np.array(img), 125, 255, cv2.THRESH_BINARY)
# Older versions of pytesseract need a pillow image
# Convert back if needed
img = Image.fromarray(img.astype(np.uint8))
print(pytesseract.image_to_string(img))
This printed out
5.78 / C02
Edit:
Doing just thresholding on the second image returns 11.1. Another step that can help is to set the page segmentation mode to "Treat the image as a single text line." with the config --psm 7. Doing this on the second image returns 11.1 "202 ', with the quotation marks coming from the partial text at the top. To ignore those, you can also set what characters to search for with a whitelist by the config -c tessedit_char_whitelist=0123456789.%. Everything together:
pytesseract.image_to_string(img, config='--psm 7 -c tessedit_char_whitelist=0123456789.%')
This returns 11.1 202. Clearly pytesseract is having a hard time with that percent symbol, which I'm not sure how to improve on that with image processing or config changes.