tesseract ocr is not working on image which have text length of only 2 or less. Works fine for Image with text length greater than 3

Question

import pytesseract  
from PIL import Image

def textFromTesseractOCR(croppedImage):
    for i in range(14):
        text = pytesseract.image_to_string(croppedImage, lang = 'eng', boxes = False  ,config = '--psm '+ str(i) +' --oem 3')     
        print("PSM Mode", i)
        print("Text detected: ",text)

imgPath = "ImagePath"   #you can use image I have uploaded 
img = Image.open(imgPath)

textFromTesseractOCR(img)

I am working on extracting Table data from PDF. For this I am converting pdf to png. Detecting Lines, ascertaining table by line intersection and then cropping individual cells to get their text.

This all works fine, but tesseract is not working on cells image which has text of length 2 or less.

Works for this image:

Result from tesseract:

Does not work for this image:

Result from tesseract: return empty string. It also returns empty for numbers of text length 2 or less.

I have tried resizing the image(which I knew wouldn't work), also tried appending dummy text to the image but the result was bad(was working only for few and I didn't the exact location to append the dummy text in the image)

It would be great if someone could help me with this.

Answer 1

So I finally came with a workaround for this situation. The situation being tesseract-OCR giving empty string when the image contains only 1 or 2 length string(eg "1" or "25").

To get output in this situation I appended the same image multiple time at the original image so as to make its length greater than 2. For example, if the original image contained only "3", I appended "3" image(the same image) 4 more times and thereby making it an image which contains the text "33333". We then give this image to tesseract which gives output "33333"(most of the times).Then we just have to replace space with blank in the text output from the Tesseract and divide the resulting string length by 5 to get the index up to which we would want to text out from the whole text.

Please see code for reference, hope this helps:

import pytesseract   ## pip3 install pytesseract

Method which calls tesseract for OCR or calls our workaround code if we get an empty string from tesseract output.

def textFromTesseractOCR(croppedImage):
    text = pytesseract.image_to_string(croppedImage)
    if text.strip() == '':    ### program that handles our problem
        if  0 not in croppedImage:
            return ""
        yDir = 3
        xDir = 3
        iterations = 4
        img = generate_blocks_dilation(croppedImage, yDir, xDir, iterations) 
        ## we dilation to get only the text portion of the image and not the whole image 
        kernelH = np.ones((1,5),np.uint8)
        kernelV = np.ones((5,1),np.uint8)
        img = cv2.dilate(img,kernelH,iterations = 1)
        img = cv2.dilate(img,kernelV,iterations = 1)
        image = cropOutMyImg(img, croppedImage)
        concateImg = np.concatenate((image, image), axis = 1)
        concateImg = np.concatenate((concateImg, image), axis = 1)
        concateImg = np.concatenate((concateImg, image), axis = 1)
        concateImg = np.concatenate((concateImg, image), axis = 1)
        textA = pytesseract.image_to_string(concateImg)
        textA = textA.strip()
        textA = textA.replace(" ","")
        textA = textA[0:int(len(textA)/5)]
        return textA
    return text

Method for dilation.This method is used to dilate only the text region of the image

def generate_blocks_dilation(img, yDir, xDir, iterations):
    kernel = np.ones((yDir,xDir),np.uint8)
    ret,img = cv2.threshold(img, 0, 1, cv2.THRESH_BINARY_INV)
    return cv2.dilate(img,kernel,iterations = iterations)

Method to crop the dilated part of the image

def cropOutMyImg(gray, OrigImg):
    mask = np.zeros(gray.shape,np.uint8) # mask image the final image without small pieces
    _ , contours, hierarchy = cv2.findContours(gray,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)     
    for cnt in contours:
        if cv2.contourArea(cnt)!=0:

        cv2.drawContours(mask,[cnt],0,255,-1) # the [] around cnt and 3rd argument 0 mean only the particular contour is drawn
        # Build a ROI to crop the QR
        x,y,w,h = cv2.boundingRect(cnt)
        roi=mask[y:y+h,x:x+w]
        # crop the original QR based on the ROI
        QR_crop = OrigImg[y:y+h,x:x+w]
        # use cropped mask image (roi) to get rid of all small pieces
        QR_final = QR_crop * (roi/255)
return QR_final