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extract image of cell from pdf table PYTHON

I need to recognize written text in a table and parse it in json. I do it using python. I don't really understand how to extract photos of text from a table that is in pdf format. Because the usual table recognizer is not suitable, since written text is not recognized there. Accordingly, I need to somehow cut the cells from the table, how to do this?

if you want to extract tables and their cells you probably need a table extractor like this; 1
Then after extracting the table and its cells with their coordinates, you are allowed to pick those pixels. For example; img[x1:x2,y1:y2]
After obtaining the cell's pixels, you can use the Tesseract OCR engine to understand the texts written in image pixels.
These are the general steps that you need to follow, I can help you more if you precise your question more.

PDF format has not 'table' and 'cell'.
Covert PDF into PNG format or other raster format and use OCR as say BlackCode.

How do you convert an image into a number in python using pytesseract

I have been trying to convert an image into a string/integer using pytesseract. The only problem is every time I run the code nothing happens. I changed the image into a text image reading "TEXT" and pytesseract detected it fine. Here is what I was using in order to convert the image into a string. I also included the image that I've been using.
bal = pytesseract.image_to_string(balIm)
print(bal)
I don't know what else to try the only other thing I could think of would be to try another OCR, any help would be appreciated, thanks.

Try setting the Page Segmentation Mode (PSM) to mode 6 which will set the OCR to detect a single uniform block of text.
Specifically, do:
bal = pytesseract.image_to_string(balIm, config='--psm 6')
This should give you what you need. In fact, I tried running this on your image and it gives me what I'm looking for. Note that I downloaded your image that you provided above first and read in the image offline on my local machine:
In [8]: import pytesseract
In [9]: from PIL import Image
In [10]: balIm = Image.open('wC62s.png')
In [11]: pytesseract.image_to_string(balIm, config='--psm 6')
Out[11]: '0.03,'
As a final note to you, if you see that Tesseract doesn't quite work for you out of the box, consider trying one of their Page Segmentation Modes to help increase accuracy: https://tesseract-ocr.github.io/tessdoc/ImproveQuality#page-segmentation-method. For completeness, I'll make this available to you below.
0 Orientation and script detection (OSD) only.
1 Automatic page segmentation with OSD.
2 Automatic page segmentation, but no OSD, or OCR.
3 Fully automatic page segmentation, but no OSD. (Default)
4 Assume a single column of text of variable sizes.
5 Assume a single uniform block of vertically aligned text.
6 Assume a single uniform block of text.
7 Treat the image as a single text line.
8 Treat the image as a single word.
9 Treat the image as a single word in a circle.
10 Treat the image as a single character.
11 Sparse text. Find as much text as possible in no particular order.
12 Sparse text with OSD.
13 Raw line. Treat the image as a single text line,
bypassing hacks that are Tesseract-specific.
When you run image_to_string, specify an input parameter config that takes in a PSM you want to operate in. Try some of these until you get it to work for your image. Make sure you use --psm in the config parameter prior to executing.

How can I extract data from a handwritten, scanned PDF using Python?

So I have these PDFs that are scanned copies of a structured feedback form. The form has these checkboxes and spaces for hand written notes. I am trying to extract the data from these PDFs and save it to an unstructured CSV file.
Now using pytesseract I am able to grab the printed text (by first converting the PDF to image) but I am not able to capture the handwritten content. Is there any of doing it.
I am enclosing a sample form for reference.
!https://imgur.com/a/2FYqWJf

PyTesseract is an OCR program. It has not been trained or designed to recognize handwriting. So you have two options: 1) Retrain it for handwriting (this would be quite time-consuming and complicated though) 2) Use another library actually meant for recognizing handwriting and not printed text like this one: https://learn.microsoft.com/en-us/azure/cognitive-services/computer-vision/quickstarts/python-hand-text

Adding justified text to image in Python

I already can add text to an image using Pillow in Python. However, I want to know how I can add formatted text. In particular, I want to add a box of text to an image such that the text is center justified.
If this isn't possible using Pillow, I am open to other image manipulation libraries (including in other languages) that make overlaying formatted text on images easier.

refer to the function in this link - http://pillow.readthedocs.io/en/3.1.x/reference/ImageDraw.html#PIL.ImageDraw.PIL.ImageDraw.Draw.text
the first argument is location. you can give it based on the size of your image on which you want to add text.

Here is a simple library which does the job of text alignment using PIL:
https://gist.github.com/turicas/1455973

Capturing screenshot and parsing data from the captured image

I need to write a desktop application that performs the following operations. I'm thinking of using Python as the programming language, but I'd be more than glad to switch, if there's an appropriate approach or library in any other languages.
The file I wish to capture is an HWP file, that only certain word processors can run.
Capture the entire HWP document in an image, might span multiple pages (>10 and <15)
The HWP file contains an MCQ formatted quiz
Parse the data from the image that is separate out the questions and answers and save them as separate image files.
I have looked into the following python library, but am still not able to figure out how to perform both 1 and 3.
https://pypi.python.org/pypi/pyscreenshot
Any help would be appreciated.

If i got it correctly , you need to extract text from image.
For this one you should use an OCR like tesseract.
Before using an OCR, try to clear noises from image.
To split the image try to add some unique strings to distinguish between the quiz Q/A