I'm trying to extract data from pdf/image invoices using computer vision.For that i used ocr based pytesseract. this is sample invoice you can find code for same below
import pytesseract
img = Image.open("invoice-sample.jpg")
text = pytesseract.image_to_string(img)
print(text)
by using pytesseract i got below output
http://mrsinvoice.com
’ Invoice
Your Company LLC Address 123, State, My Country P 111-222-333, F 111-222-334
BILLTO:
fofin Oe Invoice # 00001
Alpha Bravo Road 33 Invoice Date 32/12/2001
P: 111-292-333, F: 111-222-334
client@example.net Nomecof Reps Bob
Contact Phone 101-102-103
SHIPPING TO:
eine ce Payment Terms ash on Delivery
Office Road 38
P: 111-333-222, F: 122-222-334 Amount Due: $4,170
office@example.net
NO PRODUCTS / SERVICE QUANTITY / RATE / UNIT AMOUNT
HOURS: PRICE
1 tye 2 $20 $40
2__| Steering Wheel 5 $10 $50
3 | Engine oil 10 $15 $150
4 | Brake Pad 24 $1000 $2,400
Subtotal $275
Tax (10%) $27.5
Grand Total $202.5
‘THANK YOU FOR YOUR BUSINESS
but problem is i want to extract text and segregate it into different parts like Vendor name, Invoice number, item name and item quantity. expected output
{'date': (2014, 6, 4), 'invoice_number': 'EUVINS1-OF5-DE-120725895', 'amount': 35.24, 'desc': 'Invoice EUVINS1-OF5-DE-120725895 from Amazon EU'}
I also tried invoice2data
python library but again it has many limitation. I also tried regex and opencv's canny edge detection for detecting text boxes separately but failed to achieve the expected outcome
could you guys please help me
You must do more processing, especially because BILL TO and SHIPPING TO are not aligned with the invoice table. But you can use following code as a base.
import cv2
import pytesseract
from pytesseract import Output
import pandas as pd
img = cv2.imread("aF0Dc.jpg")
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
custom_config = r'-l eng --oem 1 --psm 6 '
d = pytesseract.image_to_data(thresh, config=custom_config, output_type=Output.DICT)
df = pd.DataFrame(d)
df1 = df[(df.conf != '-1') & (df.text != ' ') & (df.text != '')]
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
sorted_blocks = df1.groupby('block_num').first().sort_values('top').index.tolist()
for block in sorted_blocks:
curr = df1[df1['block_num'] == block]
sel = curr[curr.text.str.len() > 3]
# sel = curr
char_w = (sel.width / sel.text.str.len()).mean()
prev_par, prev_line, prev_left = 0, 0, 0
text = ''
for ix, ln in curr.iterrows():
# add new line when necessary
if prev_par != ln['par_num']:
text += '\n'
prev_par = ln['par_num']
prev_line = ln['line_num']
prev_left = 0
elif prev_line != ln['line_num']:
text += '\n'
prev_line = ln['line_num']
prev_left = 0
added = 0 # num of spaces that should be added
if ln['left'] / char_w > prev_left + 1:
added = int((ln['left']) / char_w) - prev_left
text += ' ' * added
text += ln['text'] + ' '
prev_left += len(ln['text']) + added + 1
text += '\n'
print(text)
The result
bhttps//mrsinvoice.com
Lp
I |
Your Company LLC Address 123, State, My Country P 111-222-333, F 111-222-334
BILL TO:
P: 111-222-333, F: 111-222-334 m .
dlent@ccomplent
Contact Phone 101-102-103
john Doe office ayment Terms ash on Delivery
Office Road 38
P: 111-833-222, F: 122-222-334 Amount Due: $4,170
office@example.net
NO PRODUCTS / SERVICE QUANTITY / RATE / UNIT AMOUNT
HOURS, PRICE
1 | tyre 2 $20 $40
2 | Steering Wheet 5 $10 $50
3 | Engine ol 40 $15 $150
4 | Brake Pad 2a $1000 $2,400
Subtotal $275
Tax (10%) $275
Grand Total $302.5
‘THANK YOU FOR YOUR BUSINESS
You can use my code found here: https://github.com/josephmulindwa/Table-OCR-Extractor .
There is an attached PDF that will show you how to extract your table with ease.
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