In this article, we explore what OCR (Optical Character Recognition) is in invoicing and how it can speed up your workflows. Read on to learn more.
OCR (Optical Character Recognition) is a technology that converts scanned documents, PDFs or images into editable and searchable text. In the context of invoicing, OCR allows you to extract information from an invoice so that the text is editable. This reduces data entry, manual errors, and allows for faster invoicing processing.
Example: Using an OCR software for invoicing, a scanned invoice from Acme Corporation for product number ACX456 and an amount of $3,750 can be quickly converted into editable text. This way, you can easily manage and process the invoice without spending a lot of time on data entry.
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Use our 10 step framework to process invoices using OCR. Simply follow the steps below.
Gather a diverse set of invoice images from various sources to ensure comprehensive training data. This step includes sourcing invoices with different layouts, fonts, and languages to cover all potential variations.
Example: Collect 500 invoices from three companies: Google, Amazon, and Microsoft, ensuring 200 are scanned copies and 300 are digital PDFs.
Enhance image quality by performing preprocessing tasks like noise reduction, binarization, and resizing. This helps improve OCR accuracy by standardizing the input images.
Example: Convert all 500 invoices to 300 DPI resolution, apply grayscale conversion, and use Gaussian blur for noise reduction on invoices containing products like Amazon Echo and Microsoft Surface.
Identify and locate the text regions within the preprocessed images using text detection algorithms. This step isolates the areas of interest where text is present, making it easier for OCR to process.
Example: Detect text boxes in 450 out of 500 invoices, accurately identifying areas containing invoice numbers, dates, and amounts for products like Google Pixel and Amazon Kindle.
Apply OCR techniques to the detected text regions to convert the text images into machine-readable text. This involves using OCR engines like Tesseract or specialized models trained for invoices.
Example: Use Tesseract OCR to extract text from detected regions, converting "Invoice No: 12345" and "Total: $678.90" into digital text format for Google Cloud services and Microsoft Office licenses.
Refine the OCR output by correcting common errors and formatting the text. This includes spell-checking, correcting misrecognized characters, and formatting dates and amounts correctly.
Example: Convert "Inv01ce No: 12345" to "Invoice No: 12345" and reformat "07/25/2024" to "July 25, 2024" on invoices for Amazon Web Services and Google Workspace subscriptions.
Organize the extracted text into a structured format such as JSON or CSV. This step involves categorizing the data into predefined fields like invoice number, date, vendor, and total amount.
Example: Structure the extracted data into JSON format: {"invoice_number": "12345", "date": "July 25, 2024", "vendor": "Amazon", "total_amount": "$678.90"} for invoices listing products like Amazon Fire TV and Google Nest.
Verify the accuracy of the extracted and structured data against the original invoices. This step ensures that the OCR process has correctly captured and formatted all necessary information.
Example: Compare the structured data from 50 randomly selected invoices with the original documents, achieving a 95% accuracy rate for Microsoft Azure and Amazon S3 storage invoices.
Integrate the OCR system with existing software like ERP or accounting systems. This allows for automated processing of invoices and seamless data flow between systems.
Example: Integrate the OCR system with QuickBooks, enabling automatic entry of invoice data for 100 transactions per month, including Google Ads and Microsoft 365 subscriptions.
Implement mechanisms to handle OCR errors and exceptions, such as flagging uncertain data for manual review. This ensures that any inaccuracies are promptly identified and corrected.
Example: Set up an alert system to flag invoices where the total amount is unreadable or likely incorrect, reducing errors in 5% of processed invoices for Amazon Prime and Google Drive storage fees.
Regularly update and improve the OCR model and processes based on feedback and new data. This step involves retraining models, refining algorithms, and incorporating new invoice formats.
Example: Retrain the OCR model quarterly with an additional 100 invoices from new vendors like Netflix and Spotify, improving accuracy from 95% to 97%.
TechCom, InnovateCorp, and SoftWave are emerging tech companies that want to streamline their invoice processing. Here’s how they implemented our simple 10-step OCR process:
Gather a variety of invoices with different layouts and formats for comprehensive training. For example, collect a total of 500 sample invoices, with some being scanned copies and others digital PDFs, from companies similar to TechCom, InnovateCorp, and SoftWave.
Enhance the quality of your invoice images by converting them to 300 DPI, applying grayscale conversion, and using Gaussian blur to reduce noise.
Use text detection algorithms to find and highlight text areas on your invoices. For instance, identify the areas that contain invoice numbers, dates, and amounts on most of your sample invoices.
Apply OCR techniques, such as using Tesseract OCR, to convert the highlighted text areas into digital text. For example, extract "Invoice No: 12345" and "Total: $678.90" into a format that can be edited and used digitally.
Correct common OCR errors and format the text properly. For instance, change "Inv01ce No: 12345" to "Invoice No: 12345" and reformat dates like "07/25/2024" to "July 25, 2024."
Organize the extracted text into a structured format like JSON or CSV. For example, create a JSON file that includes fields such as invoice number, date, vendor, and total amount.
Check the accuracy of the extracted data by comparing it with the original invoices. For instance, verify 50 randomly selected invoices and ensure a high accuracy rate, such as 95%.
Integrate the OCR system with your current software, like ERP or accounting systems. For example, link it with QuickBooks to automate the entry of invoice data for multiple transactions each month.
Set up alerts to flag potential OCR errors, such as unreadable amounts, for manual review. This helps catch and correct errors in about 5% of the processed invoices.
Regularly update the OCR model with new data to improve accuracy. For instance, retrain the model every few months with additional invoices from different vendors like StreamTech and CloudMatrix to boost accuracy from 95% to 97%.
Implementing OCR technology in invoicing is beneficial for a number of reasons, some of the most common include:
OCR invoice processing significantly reduces the time and effort required for manual data entry. This allows employees to focus on more strategic tasks, improving overall productivity.
Manual data entry is prone to errors, which can lead to financial discrepancies. OCR technology minimizes these errors by accurately capturing data from invoices.
Automating invoice processing with OCR reduces the need for extensive manual labor. This leads to cost savings in terms of labor and error correction.
OCR enables quicker invoice processing by instantly converting scanned documents into usable data. This accelerates the accounts payable cycle, improving cash flow management.
Digital invoices processed by OCR are easily searchable and retrievable. This enhances the ability to quickly access and analyze historical data for reporting and audits.
OCR helps in maintaining accurate records and ensures that all invoice data is correctly documented. This is crucial for compliance with financial regulations and audits.
OCR solutions can handle large volumes of invoices which makes it easier to scale operations as the business grows. This adaptability is essential for businesses experiencing rapid growth or seasonal fluctuations.
We hope you now have a better understanding of the meaning of OCR for invoicing, how to implement it, and its advantages for your business. If you enjoyed this article, you might also like our article on invoice OCR.