OCR only image on one side - image

Question

I was wondering if there's a way to only OCR the document on the right (ignoring the left) without having to split the images in PS or any other image editor?
The problem is that sometimes there is text on the images. However, they are polluting my results as I only need to rear the right-hand side.
Kind regards,
O.
## PREPROCESSING (load and read images to OCR and transform them into a DataFrame)
import pytesseract as tess
from tesserocr import PyTessBaseAPI, RIL
import os
from PIL import Image
import pandas as pd
import re
import tesserocr
path = "/Users/oliviervandhuynslager/PycharmProjects/Design Centre/assets/img/" ##path to directory (folder) where the images are located
count = 0
fileName = [] #create empty list that will contain the original filenames
fullText = [] #create empty list to store the OCR results per file
for imageName in os.listdir(path):
count = count + 1
fileName.append(imageName)
# fileName.sort()#generate list from texts.
with PyTessBaseAPI(lang='eng') as api:
for imageName in os.listdir(path):
inputPath = os.path.join(path, imageName)
api.SetImageFile(inputPath)
text = api.GetUTF8Text()
print(api.AllWordConfidences())
fullText.append(text)
d = {"FILENAME":fileName, "OCR": fullText}
df = pd.DataFrame(d)
##Generate empty lists
search_material = []
search_product = []
search_manufacturer = []
search_designer = []
search_description = []
search_dimensions = []
search_packing = []
search_price = []
search_delivery = []
## -_-_-_-_-_-_-_-_-_-_-_-_-_-
count_material = 0
count_product = 0
count_maufacturer = 0
count_designer = 0
count_description = 0
count_dimension = 0
count_packing = 0
count_price = 0
## search for PRODUCT (NAME/TITLE)
for values in df["OCR"]:
try:
search_product.append((re.search(r'Product[\s\S]+', values).group()).split("\n")[0].split(":")[1])
count_product = count_product + 1
except:
search_product.append("")
df["PRODUCT"] = search_product
## search for MANUFACTURER
for values in df["OCR"]:
try:
search_manufacturer.append((re.search(r'Manufacturer[\S\s]+', values).group()).split("\n")[0].split(":")[1])
count_maufacturer = count_maufacturer + 1
except:
search_manufacturer.append("")
df["MANUFACTURER"] = search_manufacturer
## search for DESIGNER
for values in df["OCR"]:
try:
search_designer.append((re.search(r'Designer[\S\s]+', values).group()).split("\n")[0].lstrip().split(":")[1])
count_designer = count_designer + 1
except:
search_designer.append("")
df["DESIGNER"] = search_designer
## search for MATERIALS
for values in df["OCR"]:
try:
search_material.append((re.search(r'Material[\S\s]+', values).group()).split("\n")[0].lstrip().split(":")[1])
count_material = count_material + 1
except:
search_material.append("")
df["MATERIAL"] = search_material
#search for DESCRIPTION:
for values in df["OCR"]:
try:
search_description.append((re.search(r'Description[\S\s]+', values).group()).split(":")[1])
count_description = count_description + 1
except:
search_description.append("")
df["DESCRIPTION"] = search_description
#search for DIMENSIONS
for values in df["OCR"]:
try:
search_dimensions.append((re.search(r'Dimensions[\S\s]+', values).group()).split("\n")[0].split(":")[1])
count_dimension = count_dimension + 1
except:
search_dimensions.append("")
df["DIMENSIONS"] = search_dimensions
#search for PACKING
for values in df["OCR"]:
try:
search_packing.append((re.search(r'Packing[\S\s]+', values).group()).split('\n\n')[0].split(":")[1])
count_packing = count_packing + 1
except:
search_packing.append("")
df["PACKING"] = search_packing
#search for PRICE
for values in df["OCR"]:
try:
search_price.append((re.search(r'Price[\S\s]+', values).group()).split("\n")[0].split(":")[1])
count_price = count_price + 1
except:
search_price.append("")
df["PRICE"] = search_price
#search for DELIVERY DAYS
for values in df["OCR"]:
try:
search_delivery.append((re.search(r'Delivery[\S\s]+', values).group()).split("\n\n")[0].split(":")[1])
count_delivery = count_delivery + 1
except:
search_delivery.append("")
df["DELIVERY"] = search_delivery
df.drop(columns="OCR", inplace=True)
print(df)

Answer 1

If the layout of text on your image is fixed then you can simply read the full Image but pass only half of that image array to tesseract.
import cv2
img = cv2.imread(inputPath)
_, width, _ = img.shape
half = width//2
cut = img[: half: , :]
temp_path = r'path/where/you/want/your/cropped/image/to/be/saved'
cv2.imwrite(temp_path, cut)
api.SetImageFile(inputPath)
text = api.GetUTF8Text()
print(api.AllWordConfidences())
fullText.append(text)
os.remove(temp_path) #removing cut image from the directory
Alternate Approach
You can pass the image array cut to the tesseract instead of saving it and then removing it. In that case, remember to convert the image array cut to RGB format since open cv uses BGR format by default while reading images.
rgb_arr = cv2.cvtColor(cut, cv2.COLOR_BGR2RGB)
All these things can be done with PIL also. In PIL you can use crop() to extract the required part of the image. Also by default, it reads images in the RGB format and can be passed directly to tesseract if you are following the alternate approach as mentioned above

Answer 2

You can call api.SetRectangle method passing the coordinates of the right half before recognition.