How to Extract Data from Invoices?

Organizations often struggle with processing numerous invoices as the business grows. Invoices are usually designed differently by each organization as per their needs and these styles, templates, and formats are complex to process manually.

Manual processing of invoices leads to a high rate of errors and loss of time. These concerns slow down operations and hinder overall business efficiency.  But extraction structured data from invoices help organizations to make things much smoother. It helps speed up invoice processing and reduce mistakes.

Just upload an invoice into the system and give the command to extract. Instantly, the data from all relevant fields from an invoice is accurately extracted. This results in streamlined workflow and better productivity.

In this blog you’ll learn more about how to extract data from invoices, its advantages, and a step-by-step guide to achieve automated data extraction with KlearStack. 

What is Invoice Data Extraction?

Invoice data extraction is putting all this information into the system for further processing. You can do this manually or by using invoice data extraction software or tools.

The extracted data can be used for many other purposes such as accounting, financial analysis, and reporting. When this data is well structured, it becomes easy to perform these tasks.  Automated data extraction makes this happen. It efficiently captures information from all kinds of invoices, be it unstructured, or semi-structured. That is why the majority of organizations prefer data extraction tools for efficiency and smooth operations.

Manual Invoice Data Extraction vs Automated Invoice Data Extraction

Manual Invoice Data Extraction

It’s a process when the information from invoices is manually entered into a system for further processes. This process of data entry is tedious, takes up a lot of time, and also has a high probability of error occurrence. 

These errors complicate tasks like reconciling with purchase orders and bank statements. This results in delays and inaccuracies in financial reporting.

Automated Invoice Data Extraction

Automated data extraction is when the information is extracted from an invoice using Artificial Intelligence (AI), Machine Learning (ML), and Optical Character Recognition (OCR). Data can be extracted from invoices of different formats such as PDFs, emails, printed copies, and so on. The use of advanced technologies makes sure that the invoices are processed quickly and accurately. The need for manual intervention is reduced significantly in automated invoice processing.

How does Invoice Data Extraction Software or Tools work?

Automated data extraction utilizes Invoice OCR for invoice data capture. Invoice data extraction software like KlearStack has comprehensive solution for data extraction from invoices. It converts important information like names, dates, amounts, etc., into structured data. This is then integrated into accounting and Enterprise Resource Planning (ERP) systems without any hassle.

Invoice OCR enhances the end-to-end processing, right from initial data entry to archival of invoices. This ensures better efficiency and accuracy. Invoice OCR reduces manual errors and speeds up the entire process. This results in increased availability of man-hours for strategic tasks for enhancing productivity, and improving financial management.

Let’s understand how Invoice OCR works in data extraction process:

1. Pre-Processing

Pre-processing involves preparing the scanned or photographed invoice image for further analysis. This step includes actions like noise reduction, image enhancement, and alignment to ensure clarity and uniformity. These improvements help in accurate character recognition and data extraction in subsequent stages.

2. Document Classification

This stage involves identifying and categorizing the type of document being processed. In this stage, documents are classified into different categories such as invoices, receipts, purchase orders, etc. ML algorithms recognize patterns and features that are unique to each document type. This step is crucial as only after classification, pre-defined rules for extraction can be applied.

3. Automated Capture & Digitization

In automated capture and digitization, a physical invoice is converted into a digital format. This is done via OCR technology. The document is scanned and a machine-readable text version is produced. Once the data is digitized, further processing and analysis can be performed.

4. Intelligent Data Extraction and Analysis

Intelligent data extraction and analysis refers to finding and extracting important details from the digitized invoice, such as dates, amounts, vendor details, etc. Machine learning and advanced algorithms accurately locate and interpret the required fields. Once the data is extracted, it is further analyzed to check if it’s complete and correct.

5. Validation & Integration

In the step of validation and integration, the extracted data is checked thoroughly to know if it is accurate and ready for use in other systems. The data is cross-checked with the databases available. Errors are identified, and it’s ensured that the data meets predefined business rules. Once this is done, all this data is integrated into Enterprise Resource Planning (ERP) systems, accounting software, or any other platform being used.

6. RESTful API Integration

RESTful API (Application Programming Interface) integration permits the invoice OCR system to exchange data with other software applications available on the internet. Due to APIs, different systems can interact in a standardized way. This enables the integration of OCR capabilities into existing workflows. Because of this automation in data transfer, human intervention and resulting errors are minimised.

Firstly, we will try to understand, how to extract data from invoices using python and later we will use a easy tool to extract data from invoices.

How to Extract Data from Invoices using Python

Businesses aim to streamline their accounting processes by extracting data from invoices and making it structured. Python provides an efficient way to automate this extraction, with the help of its powerful libraries and tools. In this section, we will go through the steps and tools required to do invoice data extraction using Python.

Step 1: Install Required Libraries

To get started, you’ll need to install several Python libraries, These libraries facilitate data extraction from images or PDF files. The primary libraries you can use are:

1. pdfplumber,
2. Pillow,
3. pytesseract and,
4. OpenCV

You can install these libraries using PIP(Preferred Installer Program). It helps in installing and managing packages that aren’t a part of standard python library. You also need to install Tesseract-OCR, an optical character recognition tool, which is not a Python library but an external dependency.

Step 2: Extract Text from PDF Invoices

Invoices are often in PDF format. We can use pdfplumber to extract text from these files. Here’s an example of how to extract text from a PDF invoice:

import pdfplumber

def extract_text_from_pdf(pdf_path):
    with pdfplumber.open(pdf_path) as pdf:
        all_text = ''
        for page in pdf.pages:
            all_text += page.extract_text() + '\n'
    return all_text

pdf_path = 'path_to_invoice.pdf'
invoice_text = extract_text_from_pdf(pdf_path)
print(invoice_text)

Step 3: Extract Text from Image Invoices

For invoices in image format (JPG, PNG, etc.), we can use pytesseract in combination with Pillow to perform OCR. Here’s how you can extract text from an image:

from PIL import Image
import pytesseract

def extract_text_from_image(image_path):
    image = Image.open(image_path)
    text = pytesseract.image_to_string(image)
    return text

image_path = 'path_to_invoice.jpg'
invoice_text = extract_text_from_image(image_path)
print(invoice_text)

Step 4: Preprocess Images for Better Accuracy

Image preprocessing can significantly improve the accuracy of OCR. Using OpenCV, we can apply several preprocessing techniques such as grayscale conversion, noise reduction, and thresholding.

Step 5: Parsing and Structuring Extracted Data

Once the text is extracted, it needs to be parsed and structured. Regular expressions (regex) can be used to identify and extract specific fields like invoice number, date, and total amount.

Extracting data from invoices using Python involves several steps, from reading PDF or image files to preprocessing images and parsing the extracted text. By using libraries like pdfplumber, pytesseract, and OpenCV, you can automate this process, making it faster and more efficient. With this approach, businesses can streamline their invoice processing workflows, reducing manual data entry and minimizing errors.

Now it’s time to extract data from invoices using a simple tool, KlearStack (Without using a code).

Step-by-Step Guide to Extract Structured Data from Invoices Using KlearStack

Step 1: Register/Login to the software

Once you successfully register with KlearStack as a user, you will get access to the login credentials. Upon entering these, read the terms and conditions carefully, and tick the check box. After this, you will be able to login to the dashboard, where all the wonders of data extraction start!

Step 2: View the Dashboard & Upload the Document

Once logged in, you will see the dashboard from where different documents can be uploaded. From here, you can add and process various documents, including receipts, purchase orders, bills of lading, and over 12 other document types. 

KlearStack also has an excellent feature that allows bulk processing of the documents, which means you can upload multiple documents and it will process all of those simultaneously giving you speedy and meticulous results.

Click on the invoice section to proceed to the next step.

Step 3: Upload the Invoice

Click on the ‘Add new’ tab in the top right corner of the screen. KlearStack allows you to upload documents in different formats such as Electronic PDF, Word, Excel, JPG, BMP, TIFF, PNG, scanned PDF, and ZIP.

Step 4: Select the Number of Pages and Business Type

If your invoice has multiple pages, and you wish to process only a few out of those which have relevant information for your perusal, then you can select the number of pages you wish to process from your invoice. KlearStack can process multiple pages at one go, relieving you of the task of uploading pages one by one. 

Also, here you have an option to select if the invoice is for a B2B or B2C transaction, ensuring better categorization and storage of your data and streamlining your further process of data collection when it comes to final reporting.

Step 5: View added invoices

Your most recently uploaded invoice will appear on top. Uploaded documents with their extracted data appear here, providing a quick snapshot of all necessary information at any given time.

Step 6: Click to check the extracted data.

For detailed information you can click on the uploaded invoice, you will see all the fields on one part of the screen and the uploaded invoices on the other. Here you can scroll and check all the data, while comparing it with the invoice on the other side.

Step 7: Verify the Captured Information

If you wish to understand where the information has been picked from, simply click on that particular field and it will get highlighted on the uploaded invoice. If the information is incorrect, you can edit it here. This will retrain the model for future invoices from this entity.

Step 8: Click on approve, once verified.

Once you’ve verified the details are correct, click on “approve.” The models are trained to capture data quickly and accurately.

Step 9: View invoice on the dashboard.

Once approved, go back to the dashboard and you will see your invoice there with the approval sign. Here you will be able to see all the documents and data extraction done from each.

Features of KlearStack’s Invoice OCR

KlearStack utilizes advanced technology such as Invoice OCR for automating the data extraction process. By leveraging Invoice OCR, data can be extracted from both paper-based and digital invoices. This software helps streamline your business operations, as invoices are processed quickly and correctly with reduced manual interventions. Since the entire invoice processing is automated, from data entry to archival, operational efficiency significantly increases.

KlearStack puts document digitization, classification, extraction, and validation on autopilot, by offering the following:

1. Template-less Solution:

KlearStack’s Invoice OCR offers a template-less solution that can accurately extract data from any new invoice layout without requiring model retraining. 

The system can handle many different invoice designs, adjusting to new layouts as needed. This saves time and resources spent on manual template creation.

2. Multi-lingual Support:

In KlearStack 50 languages are supported for invoice data extraction. Which includes English, Hindi, Marathi, French, German, Chinese, Japanese, and more.

When businesses operate with suppliers in different regions and countries, this feature becomes essential. Multi-lingual support ensure that language barriers do not hamper efficient invoice processing.

3. Line-item Data Extraction:

Because of this feature all the vital line-items are extracted in detail from the invoices with utmost precision. These items include product descriptions, quantities, unit prices, total amounts etc.

This detailed extraction of data is crucial for financial analysis and inventory management. It is also essential for ensuring that the invoiced items match purchase orders and received goods.

4. Multi-page Data Extraction:

KlearStack’s Invoice OCR is seamlessly compatible with multi-page invoices, ensuring that data is extracted with precision across all pages.
This capability is particularly important for large invoices or those with extensive itemized lists, ensuring that no critical information is overlooked.

5. Bulk Invoice Processing:

Simultaneous processing of multiple invoices becomes possible with the batch processing feature of KlearStack’s Invoice OCR. This speeds up the processing of invoices significantly, making it possible to handle high volumes of invoices with improved efficiency.

Hundreds or even thousands of invoices in one go can be processed at one go, drastically reducing the time required for manual processes.

6. Straight-through Processing (STP):

Straight-through Processing makes it possible to automate end-to-end invoice processing, which improves the speed and cost-effectiveness of financial transactions.

Since the process is entirely automated, manual intervention in minimised to a great extent. This allows faster turnaround times and reduction in the risk of errors. It also improves efficiency of financial operations and cash flow management.

7. Seamless Integration:

KlearStack’s Invoice OCR can be easily integrated with your existing ERP and accounting systems. Because of this integration, extracted and validated invoice data directly gets transferred into financial management systems. This cuts down on the need for manual data entry. This boosts the overall productivity and efficiency of the processes.

Schedule a KlearStack Demo [Live Blind Accuracy Test in Call]

KlearStack provides an easy and efficient way to get your invoice data extraction process in place. It ensures all your data is extracted precisely and stored in appropriate categories for further utilization. Upload invoices of any format, whether semi-structured or unstructured and benefit from KlearStack’s support for over 50 languages. Our expertly trained models will extract your data seamlessly.

Wondering how KlearStack makes it possible?  We invite you to come to us for a demo using any invoice format, structured or unstructured, and witness KlearStack’s exceptional information processing capabilities. Simplify data extraction from documents with ease. Yes, this is a blind test we are ready to undertake!

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