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Table extraction from images helps turn pictures of tables into usable data. This saves time and avoids manual typing.
1. Load the image containing the table. 2. Use a table detection model or algorithm to find table boundaries. 3. Extract the table cells by detecting lines or using OCR. 4. Convert the extracted cells into structured data like CSV or JSON.
Step 2 often uses deep learning models trained to detect tables.
OCR (Optical Character Recognition) reads text inside each cell.
import cv2 import pytesseract image = cv2.imread('table_image.png') # Use OpenCV to detect table lines # Use pytesseract to extract text from cells
from paddleocr import PaddleOCR ocr = PaddleOCR() result = ocr.ocr('table_image.png') # PaddleOCR can detect tables and extract text directly
This code loads an image, detects table lines using image processing, finds cells, and extracts text using OCR.
It prints each cell's position and text.
import cv2 import numpy as np import pytesseract # Load image image = cv2.imread('table_sample.png') gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # Threshold to get binary image _, binary = cv2.threshold(gray, 150, 255, cv2.THRESH_BINARY_INV) # Detect horizontal lines horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (40,1)) horizontal_lines = cv2.morphologyEx(binary, cv2.MORPH_OPEN, horizontal_kernel) # Detect vertical lines vertical_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1,40)) vertical_lines = cv2.morphologyEx(binary, cv2.MORPH_OPEN, vertical_kernel) # Combine lines to get table mask table_mask = cv2.add(horizontal_lines, vertical_lines) # Find contours of table cells contours, _ = cv2.findContours(table_mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) cells = [] for cnt in contours: x, y, w, h = cv2.boundingRect(cnt) if w > 20 and h > 20: # filter small boxes cell_img = image[y:y+h, x:x+w] text = pytesseract.image_to_string(cell_img, config='--psm 7').strip() cells.append({'position': (x, y, w, h), 'text': text}) # Sort cells by position (top to bottom, left to right) cells_sorted = sorted(cells, key=lambda c: (c['position'][1], c['position'][0])) # Print extracted text from cells for cell in cells_sorted: print(f"Cell at {cell['position']}: '{cell['text']}'")
Good lighting and clear images improve extraction accuracy.
Complex tables with merged cells may need advanced models.
Preprocessing like noise removal helps OCR results.
Table extraction turns images of tables into editable data.
It uses image processing to find table structure and OCR to read text.
This helps automate data entry and analysis from pictures or scans.
table extraction from images in computer vision?cells_text?
import cv2
import pytesseract
image = cv2.imread('table.png', 0)
_, thresh = cv2.threshold(image, 128, 255, cv2.THRESH_BINARY_INV)
contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cells_text = []
for cnt in contours:
x, y, w, h = cv2.boundingRect(cnt)
cell_img = image[y:y+h, x:x+w]
text = pytesseract.image_to_string(cell_img, config='--psm 6')
cells_text.append(text.strip())
print(type(cells_text))cells_text is initialized as an empty list and text from each detected cell is appended to it.cells_textcells_text collects multiple strings in a list, its type remains list.