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A Quick Insight into Document OCR

In this article, we help you get an insight into automated data extraction with OCR using Tessaract. We’ll walk you through the entire workflow and discuss advantages and disadvantages of this DIY approach. In the end, we help you figure out what's better for your business - building data capture capabilities in-house or opting for an automated data extraction solution. 

Let’s jump right into it:-

What is document OCR?

Paperwork is hectic and time-consuming, especially when there are loads of pdf to scan and extract data from. In such scenarios, you cannot glide down to every single pdf and pick out the content of your choice. Document OCR makes it easier to extract data from these files and arrange in a format where it can be analyzed and processed for different purposes.

Since its inception, Document OCR is used by many users worldwide. The easy adaptability of smartphones and other devices has led to the rapid expansion of OCR. Not to forget the API that helps extract text to the targeted device.

Optical Character Recognition technology can help users identify and fetch texts.  Most of them fall under the category of pdf to Word OCR. Here, the pdf documents get converted into readable text form.  

How does document OCR work?

Here's how a reader can read the content of the pdf files using OCR. In this example, we’re using Tessaract, which is a free OCR engine released under Apache license. 

Step 1: The installation procedure

pip3 install PIL
pip3 install pytesseract
pip3 install pdf2image
sudo apt-get install tesseract-ocr

Step 2: Import the required libraries

from PIL import Image
import pytesseract
import sys
from pdf2image import convert_from_path
import os

Step 3: Provide the appropriate path of pdf

# Path of the pdf
PDF_file = "input.pdf"

Step 4: Store the required PDF pages in a variable

pages = convert_from_path(PDF_file, 500)

Step 5: Provide an image counter

image_counter = 1

Step 6: Iterate all the pages

for page in pages:
# Declare filename for each page of PDF as JPG
# For each page, filename will be:
# PDF page 1 -> page_1.jpg
# PDF page 2 -> page_2.jpg
# ....
# PDF page n -> page_n.jpg
filename = "page_" + str(image_counter) + ".jpg"

Step 7: Save the image of the page on the system, 'JPEG')

Step 8: Provide a counter for incrementing filename

image_counter = image_counter + 1

You need to recognise the text once you extract the images from the required pdf. For that, you need to continue as per the code given below:

Step 9: Variable to count the total number of pages

Filelimit = image_counter - 1

Step 10: Creating a Text file

outfile = "out_text.txt"

Step 11: Opening the file in append mode the image content get into the same file.

f = open(outfile, "a")

Step 12: Iterate the value to n no. of pages

for i in range(1, filelimit + 1):
 filename = "page_" + str(i) + ".jpg"

Step 13: Recognize the text using pytesseract

text= str(((pytesseract.image_to_string(

Step 14: Replace, write and then close the text form.

text = text.replace('-\n', '')    

The above can very well identify the pdf and convert the text from a given file.

Best example to justify OCR

OCR is useful to different businesses for different use-cases, but in this example, we'll limit ourselves to underwriters only.

Underwriters need to process a large set of tax documents for mortgage loans, personal loans, or small business loans. In such scenarios, lenders demand accurate data reports. Any slight errors in extraction can result in a lack of quality data supply.

Based on the parameters such as adaptability and accuracy, there are some requirements to be fulfilled such as ability to process diverse layouts and templates. Therefore, picking an OCR based automated tax document processing solution that works for both structured and semi-structured forms is the best fit.  

How does OCR work with structured and semi-structured forms?

There are two types of forms that OCR deals with, i.e., structured and semi-structured. While structured forms clearly describe documents having text blocks with fields in the same place. But in the case of semi-structured forms, the key identifiers and checkboxes differ due to location changes with the data fields.

OCR works wonders with structured forms as the data stays at the same position on each page. This allows higher data extraction accuracy.

In semi-structured forms, sometimes, the data typed next or close to the vertical lines can be neglected by the OCR engine. There can be several other issues with semi-structured form processing where the solution captures incorrect information assigned to a key-identifier. These limitations are overcome with anchor-text based OCR extraction and by employing NLP based ML models.

Use Case of Document OCR

The most widely use case of OCR comes in the case of extracting machine-readable data. The text of the document is editable through Microsoft Word and Google Docs. However, it must go through the process of scanning the paper document.

The OCR use case is not only limited to data extraction, but it can be a solution for the below cases as well:

  • Passport recognition in airports
  • Extracting connecting information from business cards
  • Overpowering CAPTCHA anti-bot approaches
  • Constructing electronic documents search like an eBook.
  • Traffic sign recognition
  • Data entry as per business requirements

What's new in the OCR?

There's no doubt that OCR has been a milestone in the automated document processing journey. But there is always room for further integration and development. From being a scanning machine to smartphone software, OCR has an indelible impact on users. But there might be a question hovering inside your head, "Is there anything new going on in OCR technology?"

Well, it's true technically. Different OCR software are trying to improve on their features, data extraction accuracy, and straight through processing. Recently, a lot of attention has been given to ICR (Intelligence Character Recognition). Being an advanced form of the OCR software, ICR enhances the interpretation of texts to transcribe them into standardized formats.  

Several OCR software are integrated through API. There has been a huge contribution of the latest trending technologies such as Machine Learning and Artificial Intelligence in shaping modern document data capture technologies. 

Advantages of OCR

There are multiple advantages of OCR in data extraction and data entry. It helps enterprises in improving the efficacy and efficiency of the data work. The ability to quickly scan through a massive pile of content is quite useful for those working on it. With high-level document inflow and volume scanning, the work gets done in a quick span. Following are the advantages of using OCR-

1. High accuracy

OCR can be a great asset in reducing even the slightest inaccuracy. There are many OCR software in the market that fulfil this criterion.

2. Cost reduction

There is lesser manpower required to operate upon the OCR. It also reduces the other costs involving copying, printing, and shipment of data.  

3. High productivity

Quick data retrieval can help the OCR software ensure higher efficiency. Now, no need to make multiple record rooms to access the document as it can be easily accessible via computer.

Limitations of document OCR

OCR is an essential data extraction technology. But there is always room for more modifications. There are some limitations associated with the technology: -

1. Font size

OCR may not be compatible in converting characters with very large and small font sizes.

2. Case sensitive (in editing)

OCR text can find it difficult to identify the letter case, whether uppercase or lowercase. In such scenarios, both letter cases are alike.

3. Uni-Dimensional

OCR recognizes and extracts special characters horizontally. It serves as uni-dimensional before and after the set of characters.

DIY or an end-to-end automated data extraction solution?

If you're processing simple documents in small numbers (say, less than 1,000 documents a month) which can be easily templatized with a rule-based approach, building in-house document capture capabilities is the right choice. However, as the complexity and sheer number of documents to be processed increases, the DIY approach results in slow and inaccurate data extraction.

Businesses often try to build an automated data extraction solution in-house only to realize that there are more efficient, versatile, and customizable solutions out there in the market costing much less than the operational cost of an in-house solution. 

Don’t worry, we’re not leaving you in the middle. In fact, we’re leaving you with resources to help you find the best-suited automated document processing approach for your business:-

Resource 1 - What is Optical Character Recognition? 

Resource 2 - What is Intelligent Document Processing? 

Resource 3 - What’s the difference between an IDP solution and OCR? 

Resource 4 - Commonly asked questions about OCR

Resource 5 - How to choose a document processing software for your business?

Resource 6 - Comparison of best automated data extraction solutions available in the market

Happy researching.

Suggested Case Study
Automating Portfolio Management for Westland Real Estate Group
The portfolio includes 14,000 units across all divisions across Los Angeles County, Orange County, and Inland Empire.
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Pankaj Tripathi
Written by
Pankaj Tripathi

Helping enterprises capture data for analytics and decisioning

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