I have data which in a structured table image. The data is like below:

I tried to extract the text from this image using this code:

import pytesseract
from PIL import Imagevalue=Image.open("data/pic_table3.png")
text = pytesseract.image_to_string(value, lang="eng")    
print(text)

and, here is the output:

EA Domains

Traditional role

Future role

Technology e Closed platforms ¢ Open platforms

e Physical e VirtualizedApplicationsand |e Proprietary e Inter-organizationalIntegration e Siloed compositee P2P integrations applications

e EAI technology e Software asa Service

e Enterprise Systems e Service-Oriented

e Automating transactions Architecture

e “Informating”

interactions

However, the expected data output should be aligned according to the column and row. How can I do that?

You must preprocess the image to remove the table lines and dots before throwing it into OCR. Here's an approach using OpenCV.

1. Load image, grayscale, and Otsu's threshold
2. Remove horizontal lines
3. Remove vertical lines
4. Dilate to connect text and remove dots using contour area filtering
5. Bitwise-and to reconstruct image
6. OCR

Here's the processed image:

Result from Pytesseract

EA Domains Traditional role Future role
Technology Closed platforms Open platforms
Physical Virtualized
Applications and Proprietary Inter-organizational
Integration Siloed composite
P2P integrations applications
EAI technology Software as a Service
Enterprise Systems Service-Oriented
Automating transactions Architecture
“‘Informating”
interactions

Code

import cv2
import pytesseractpytesseract.pytesseract.tesseract_cmd = r"C:\Program Files\Tesseract-OCR\tesseract.exe"# Load image, grayscale, and Otsu's threshold
image = cv2.imread('1.png')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]# Remove horizontal lines
horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (50,1))
detect_horizontal = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, horizontal_kernel, iterations=2)
cnts = cv2.findContours(detect_horizontal, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:cv2.drawContours(thresh, [c], -1, (0,0,0), 2)# Remove vertical lines
vertical_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1,15))
detect_vertical = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, vertical_kernel, iterations=2)
cnts = cv2.findContours(detect_vertical, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:cv2.drawContours(thresh, [c], -1, (0,0,0), 3)# Dilate to connect text and remove dots
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (10,1))
dilate = cv2.dilate(thresh, kernel, iterations=2)
cnts = cv2.findContours(dilate, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:area = cv2.contourArea(c)if area < 500:cv2.drawContours(dilate, [c], -1, (0,0,0), -1)# Bitwise-and to reconstruct image
result = cv2.bitwise_and(image, image, mask=dilate)
result[dilate==0] = (255,255,255)# OCR
data = pytesseract.image_to_string(result, lang='eng',config='--psm 6')
print(data)cv2.imshow('thresh', thresh)
cv2.imshow('result', result)
cv2.imshow('dilate', dilate)
cv2.waitKey()