Automating Document Processing with Optical Character Recognition (OCR)

Did you know that businesses spend an average of 6% of their total revenue managing paper documents? First of all, document problems can cause employees to lose up to 25% of their productivity, and around 7.5% of paper documents are estimated to go missing, leading to wasted time searching for them. Moreover, when documents can't be found, a staggering 83% of employees end up recreating them, resulting in duplication of work and multiple versions of the same document. A recent study by Forrester Consulting for Adobe revealed that 97% of companies with limited digital document systems, and 72% using both paper and digital processes, reported that these issues harmed productivity.

Source: Forrester Consulting

But how can you ensure your OCR implementation delivers the best results? Here’s a quick breakdown of the best use cases for OCR as well as factors that impact OCR performance and how you can optimize your documents for better accuracy.

The Impact of OCR on Business Efficiency

The implementation of optical character recognition technology directly leads to significant savings that an organization can achieve. More studies have shown that the adoption of OCR can lead to the release of significant resources to support continued innovation and growth by businesses.

With its ability to access documents quickly and accurately, OCR lets employees pay less concentration on routine tasks that eat up a chunk of their time and rather shift their resources to higher-order strategic initiatives. This can have a huge impact on morale and productivity because teams will be able to focus their energy on meaningful projects instead of being bogged down by paperwork.

It also tends to improve the attitude of employees since organizations that implement document automation, including OCR, are supposed to benefit from improved work satisfaction. When workers obtain the information they need easily, they become more motivated and productive, thus improving their attitude at the workplace.

Real-World Use Cases of OCR

To illustrate how OCR can transform business processes, let’s explore some real-world use cases across different industries:

1. Healthcare

In the healthcare sector, patient records and medical forms can pile up quickly, consuming time and resources. OCR technology enables healthcare providers to digitize handwritten notes, prescriptions, and patient records. 

If you own a hospital, you can implement OCR to convert handwritten physician notes into electronic health records (EHR). This not only reduces the time you spend on manual data entry but also minimizes the risk of errors, ensuring that patient information is accurate and readily accessible.

2. Finance and Banking

The finance and banking industries are heavily reliant on documentation, from loan applications to transaction records. By using OCR, financial institutions can automate the extraction of key data from various forms, including checks, invoices, and contracts.

For example, a bank can utilize OCR to process loan applications, extracting essential information like names, addresses, and income figures from physical documents. This automation can decrease processing time from weeks to just days, significantly improving customer satisfaction.

3. Legal Industry

In the legal field, managing contracts, case files, and legal documents can be a daunting task. OCR technology helps law firms convert paper-based documents into searchable digital formats, allowing attorneys to quickly retrieve critical information. The International Legal Technology Association (ILTA) estimates that law firms can save up to $10,000 annually per attorney by using IDP to automate repetitive tasks like document review, contract analysis, and e-discovery.

Law firms mainly use OCR to digitize stacks of legal briefs and case law, enabling their lawyers to search through thousands of pages in seconds saving time while also empowering attorneys to prepare cases more effectively, leading to better outcomes for clients.

4. Manufacturing

In manufacturing, managing inventory and supply chain documents is crucial for efficiency. OCR can automate the data entry process for inventory records, shipping documents, and supplier invoices. By implementing OCR, companies automatically extract relevant information from these documents and update their inventory management system in real-time reducing the likelihood of human error and streamlining the procurement process.

5. Education

Finally, educational institutions generate a vast amount of paperwork, from student applications to assessment forms. OCR usually streamlines the admissions process by digitizing student applications, allowing administrators to search for and evaluate candidates more efficiently.

Optimizing OCR for Maximum Efficiency

For your OCR system to work effectively, attention to detail is paramount. Here are essential strategies to enhance its performance:

Focus on Quality

The foundation of effective OCR lies in the quality of your input documents. Clear, machine-printed text is vital for accurate recognition. High-quality documents - those generated by modern word processors or printers - lead to optimal results.

However, many factors can hinder OCR accuracy. Skewed images, distortion, and noise can complicate the recognition process. Speckles, streaks, and watermarks can create confusion for the OCR engine, making it challenging to determine between text and background. So, in short, you’ve to ensure your documents are clean and clear as it’s crucial for achieving high accuracy.

Choose the Right File Size and Format

Document size and format make a big difference in the performance of OCR. Larger-sized files with complex color details increase the processing time because they put a greater load on the system resources. PDFs are of varying qualities depending on how they were created; generally, those that are created directly from text yield better OCR results than those created from a scanned image of text.

OCR often prefers TIFF files due to their ability to maintain high image quality and essential DPI (dots per inch) information, which is critical for accurate recognition. On the other hand, JPG files can suffer from excessive compression, leading to a loss of clarity. Using the right format can make all the difference in how well your OCR system performs.

Select Suitable Fonts and DPI Settings

While OCR engines can recognize a range of fonts, using standard fonts like Arial or Times New Roman can enhance results. Unusual fonts or tiny font sizes can confuse the system, leading to errors. A DPI setting of 200 to 300 is typically ideal, striking a balance between file size and clarity.

However, higher DPI settings would come in handy for enhancing recognition in cases of small fonts or more complex languages. Although this increases file size and the time for processing, 300 DPI usually offers the best mix of performance and accuracy for most business documents.

Simplify Color Documents

Color documents can be processed effectively with OCR, but the color choices matter significantly. Darker text colors are much easier for OCR engines to recognize compared to lighter ones. High-contrast combinations, such as black text on a white background, generate the best results. However, faded text or documents with colorful backgrounds can confuse the system, leading to misreading characters.

To boost accuracy, consider simplifying color documents or converting them to grayscale when feasible so that it can help the OCR engine better distinguish between text and background.

Address Character Substitution Challenges

One more common challenge with OCR is character substitution or when similar-looking characters are mistaken for one another. For instance, capital "O," lowercase "o," and the number "0" can easily be confused. While many OCR tools come with mechanisms to correct these errors, occasional mistakes can still occur if not defining key terms within your document's structure. 

Test in Real-World Scenarios

And before rolling out OCR solutions across your organization, testing under real-world conditions is vital. The performance of OCR can vary based on document complexity, file size, and the hardware used for processing. Since OCR engines analyze documents pixel by pixel, complex documents can take longer to process.

Thus test with actual pages from your day-to-day operations rather than relying solely on sample documents. This approach ensures that the system can handle the variety of documents your team deals with regularly, even those that may be less than perfect.

Final Words

By paying close attention to document quality, file format, fonts, DPI, and color, businesses can achieve better OCR performance and accuracy. Automating document processing with OCR is a smart move for any organization looking to cut costs, boost efficiency, and stay ahead in today’s competitive landscape. Thus, the key aspect is investing in high-quality input and fine-tuning the details to ensure that your OCR implementation works to its full potential.