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.
Related
I am using Tesseract OCR trying to convert a preprocessed license plate image into text, but I have not had much success with some images which look very much OK. The tesseract setup can be seen in the function definition. I am running this on Google Colab. The input image is ZG NIVEA 1 below. I am not sure if I am using something wrong or if there is a better way to do this - the result I get form this particular image is A.
!sudo apt install -q tesseract-ocr
!pip install -q pytesseract
import pytesseract
pytesseract.pytesseract.tesseract_cmd = r'/usr/bin/tesseract'
import cv2
import re
def pytesseract_image_to_string(img, oem=3, psm=7) -> str:
'''
oem - OCR Engine Mode
0 = Original Tesseract only.
1 = Neural nets LSTM only.
2 = Tesseract + LSTM.
3 = Default, based on what is available.
psm - Page Segmentation Mode
0 = Orientation and script detection (OSD) only.
1 = Automatic page segmentation with OSD.
2 = Automatic page segmentation, but no OSD, or OCR. (not implemented)
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.
'''
tess_string = pytesseract.image_to_string(img, config=f'--oem {oem} --psm {psm}')
regex_result = re.findall(r'[A-Z0-9]', tess_string) # filter only uppercase alphanumeric symbols
return ''.join(regex_result)
image = cv2.imread('nivea.png')
print(pytesseract_image_to_string(image))
Edit: The approach in the accepted answer works for the ZGNIVEA1 image, but not for others, e.g. , is there a general "font size" that Tesseract OCR works with best, or is there a rule of thumb?
by applying gaussian blur before OCR, I ended up with the correct output. Also, you may not need to use regex by adding -c tessedit_char_whitelist=ABC.. to your config string.
The code that produces correct output for me:
import cv2
import pytesseract
image = cv2.imread("images/tesseract.png")
config = '--oem 3 --psm 7 -c tessedit_char_whitelist=ABCDEFGHIJKLMNOPQRSTUVWXYZ'
image = cv2.resize(image, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC)
image = cv2.GaussianBlur(image, (5, 5), 0)
string = pytesseract.image_to_string(image, config=config)
print(string)
Output:
Answer 2:
Sorry for the late reply. I tested the same code on your second image, and it gave me correct output, are you sure you removed the config part since it doesnt allow numbers in my whitelist.
Most accurate solution here is training your own tesseract model on license plates' fonts (FE-Schrift) instead of tesseract's default eng.traineddata model. It will definetly increase the accuracy since it only contains your case's characters as output classes. As answer to your latter question, tesseract does some preprocessing before the recognition process (thresholding, morphological closing etc.) that is why image it is so sensitive to letter size. (smaller image: contours are closer to eachother so closing will not seperate them).
To train tesseract on custom font you can follow the official docs
To read more about Tesseract's theoritical part you can check these papers:
1 (relatively old)
2 (newer)
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.
I'm guessing this is because the images I have contain text on top of a picture. pytesseract.image_to_string() can usually scan the text properly but it also returns a crap ton of gibberish characters: I'm guessing it's because of the pictures underneath the text making Pytesseract think they are text too or something.
When Pytesseract returns a string, how can I make it so that it doesn't include any text unless it's certain that the text is right.
Like, if there a way for Pytesseract to also return some sort of number telling me how certain the text is scanned accurately?
I know I kinda sound dumb but somebody pls help
You can set a character whitelist with config argument to get rid of gibberish characters,and also you can try with different psm options to get better result.
Unfortunately, it is not that easy, I think the only way is applying some image preprocessing and this is my best:
Firstly I applied some blurring to smoothing:
import cv2
blurred = cv2.blur(img,(5,5))
Then to remove everything except text, converted image to grayscale and applied thresholding to get only white color which is the text color (I used inverse thresholding to make text black which is the optimum condition for tesseract ocr):
gray_blurred=cv2.cvtColor(blurred, cv2.COLOR_BGR2GRAY)
ret,th1 = cv2.threshold(gray_blurred,239,255,cv2.THRESH_BINARY_INV)
and applied ocr then removed whitespace characters :
txt = pytesseract.image_to_string(th1,lang='eng', config='--psm 12')
txt = txt.replace("\n", " ").replace("\x0c", "")
print(txt)
>>>"WINNING'OLYMPIC GOLD MEDAL IT'S MADE OUT OF RECYCLED ELECTRONICS "
Related topics:
Pytesser set character whitelist
Pytesseract OCR multiple config options
You can also try preprocessing your image to let pytesseract work more accurate and if you want to recognize meaningful words you can apply spell check after ocr:
https://pypi.org/project/pyspellchecker/
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 have used the following PyTorch implementation of EAST ( Efficient and Accurate Scene Text Detector) to identify and draw bounding boxes around text in a number of images and it works very well!
However, the next step of OCR which I am trying with pytesseract in order to extract the text form these images and converting them to strings - is failing horribly. Using all possible configurations of --oem and --psm, I am unable to get pytesseract to detect what appears to be very clear text, for example:
The recognized text is below the images. Even though I have applied contrast enhancement, and also tried dilating and eroding, I cannot get tesseract to recognize the text. This is just one example of many images where the text is even larger and clearer. Any suggestions on transformations, configs, or other libraries would be helpful!
UPDATE: After trying Gaussian blur + Otso thresholding, I am able to get black text on white background (apparently which is ideal for pytesseract), and also added Spanish language, but it still cannot read very plain text - for example:
reads as gibberish.
The processed text images are and
and the code I am using:
img_path = './images/fesa.jpg'
img = Image.open(img_path)
boxes = detect(img, model, device)
origbw = cv2.imread(img_path, 0)
for box in boxes:
box = box[:-1]
poly = [(box[0], box[1]),(box[2], box[3]),(box[4], box[5]),(box[6], box[7])]
x = []
y = []
for coord in poly:
x.append(coord[0])
y.append(coord[1])
startX = int(min(x))
startY = int(min(y))
endX = int(max(x))
endY = int(max(y))
#use pre-defined bounding boxes produced by EAST to crop the original image
cropped_image = origbw[startY:endY, startX:endX]
#contrast enhancement
clahe = cv2.createCLAHE(clipLimit=4.0, tileGridSize=(8,8))
res = clahe.apply(cropped_image)
text = pytesseract.image_to_string(res, config = "-psm 12")
plt.imshow(res)
plt.show()
print(text)
Use these updated data files.
This guide criticizes out-of-the box performance (and maybe the accuracy could be affected too):
Trained data. On the moment of writing, tesseract-ocr-eng APT package for Ubuntu 18.10 has terrible out of the box performance, likely because of corrupt training data.
According to the following test I did, using the updated data files seems to provide better results. This is the code I used:
import pytesseract
from PIL import Image
print(pytesseract.image_to_string(Image.open('farmacias.jpg'), lang='spa', config='--tessdata-dir ./tessdata --psm 7'))
I downloaded spa.traineddata (your example images have Spanish words, right?) to ./tessdata/spa.traineddata. And the result was:
ARMACIAS
And for the second image:
PECIALIZADA:
I used --psm 7 because here it says that it means "Treat the image as a single text line" and I thought that should make sense for your test images.
In this Google Colab you can see the test I did.