This data-driven world has generated immense volumes of documents for businesses to deal with daily. These could include PDFs, KYC forms, invoices, and much more. Very often, the information must be extracted cautiously to find its place in business workflows.
In all this, Optical Character Recognition technology plays a very critical role. The OCR enables a company to convert scanned images and documents into machine-readable text that can automate data entry and analysis, thus simplifying operations.
However, ensuring that your OCR consistently provides 99% accurate data is very important. Various factors affect the accuracy of OCR software, reducing its accuracy.
If you feel your OCR software is not providing accurate results and want to improve its accuracy, then this article is for you.
This article will give you all the necessary information that you can use to increase the accuracy of your OCR software in no time.
What is OCR, and Why is Accuracy Critical?
The OCR technology converts the printed or handwritten text in an image or document into editable, searchable data. Its uses run the gamut, from digitizing invoices to automating KYC processes and dealing with complex business forms. Attribution: OCR implementations can be a success or failure based on how accurate it is.
Your OCR system needs to be highly accurate… High accuracy rates decrease the need for manual corrections and more efficient operations and provide consistency in data output.
On the other hand, low OCR accuracy results in increased data entry errors and leads to misinterpretation of critical documents along with workflow logistics. Senior officers overseeing large amounts of data will not only look for an improvement in OCR accuracy but also find it necessary for business performance.
Before discussing strategies to improve OCR accuracy, let’s understand how OCR software works. Without understanding how it functions, you cannot improve its performance and use it at its highest potential.
Also Read: Predictive Analytics in Action: How Businesses Are Using Past Data to Predict Future Outcomes
Unraveling the Process: How OCR Transforms Documents into Editable Text
The step-by-step mechanism of OCR technology begins with scanning and converting text into an editable format. Here’s the step-by-step guide on how the technology works:
- Digitization: A high-resolution digital image of the document is made when scanned. The scanned image is a crucial input for OCR software.
- Text Detection: This process makes differentiating between text and non-text elements (i.e., shapes, forms or images) easy. The first phase is the matching process; the software executes many characters, such as letters or numbers.
- Pattern Recognition: This final attribute matches the recognized characters with patterns stored in its database. Patterns are used to match fonts and symbols and, in some cases, languages for the software.
- Machine-Readable Text: Upon identifying text within established patterns, the software turns it into editable text in an appropriate format (e.g., a Word file, PDF or sheet) and converts it for further usage.
Modern OCR systems, such as artificial intelligence and machine learning, are better at recognizing variable types of texts like font and layout and handwritten content. With advancing technology, OCR also performs well in complex, multilingual documents. It will boost the data processing for bank statements, insurance papers and boost the process of data extraction from shipping labels.
How to Achieve Optimal OCR Accuracy: Expert Best Practices
Improving OCR accuracy requires a focused approach, mainly when your business relies on high-quality data extraction. Here are some of the most effective strategies:
Use High-Quality Documents
This is one of the easiest things you can do to improve OCR accuracy. OCR software is just as good at reading Low-resolution/Blurry images; it also confuses once it comes to character/text recognition.
Documents should be scanned at a minimum of 300 DPI. Higher DPI images will be sharper, and it will configure the OCR systems. It won’t be able to determine whether the remaining parts are letters or other characters, so OCR will not process it and move ahead, leading to data loss.
Investing in good-quality scanners is necessary if you work for a business that deals with multiples of thousands of documents. Well-aligned and clear pictures can bring down OCR mistakes and increase the accuracy rate significantly.
Optimize Document Layouts and Formatting
OCR text extraction software fails to accurately process a poorly scanned document with a complex layout, multiple columns, mixed text formats, or irregular spacing. OCR software often struggles to interpret where text begins and ends in these scenarios. You must ensure standard and simple document layouts to make it easier for the OCR to perform well.
An invoice, form, business contract, and other documents should have the same straightforward look and feel throughout your organization. It will help OCR to speed up processing and increase its accuracy.
Use the most straightforward style possible, and avoid using all kinds of beauty fonts, handwritten notes or uncommon symbols because it makes it even harder for OCR to recognize the text.
Preprocessing Techniques: Cleaning Up Before OCR
Pre processing is crucial in enhancing OCR accuracy, especially for noisy images and scanned documents with skew, noise, bad lighting, etc. Preprocessing techniques mean you need to improve your scan images before putting them into the OCR system. Some of these techniques include:
Noise Reduction: Remove spots in the image that are irrelevant to OCR software (dots, streaks).
De-skewing: This process of straightening or aligning the images so that text is parallel to scanning lines.
Contrast Enhancement: By increasing the contrast between the background and text, you can help the OCR software better understand letters, blank spaces, and special characters.
These preprocessing techniques will help you increase OCR’s ability to scan documents and extract data accurately.
Also Read: What is data integration and how is it related to data analysis?
Use OCR Software with Multilingual Capabilities
If your company handles documents in different languages, then making the right choice regarding an OCR that can extract text in multiple languages is necessary. Most standard OCR systems can only process a single language or get confused when using the same document containing more than one language.
Advanced OCR systems are created to recognize characters from different languages accordingly. With the functionality of multilingual OCR capabilities, you can implement it in areas like finance and law, where companies usually work with international clients and contract writing.
Implement Batch Processing for Large-Scale Tasks
For companies handling high volumes of data, batch processing is a game-changer. By processing documents in bulk, OCR systems can extract data more consistently across large sets of files. Batch processing can also help businesses maintain accuracy since errors accumulate when handling documents individually.
Businesses that deal with large-scale invoicing, order forms, or customer applications will benefit significantly from this feature, as it helps process large volumes efficiently without sacrificing accuracy.
Regularly Update Your OCR Software
As with all technology, OCR is moving fast. Your OCR software added more advanced algorithms, better recognition of the characters and much more robust data extraction with every update. Keeping current on these software upgrades could keep your high OCR accuracy alive.
Old OCR software is not only sometimes unable to process newer document types and evolving languages — it can generate errors that will cost your business needless time and resources. Be sure to always run the latest version on your systems.
Leveraging Machine Learning for OCR Enhancement
However, the application of ML could be an essential element for the practical improvement of the accuracy of OCR over time. OCR solutions using ML will keep learning from previous failures, and the recognition patterns will continue improving with every new document encountered.
The better the amount of data the solutions process, the more intelligent they become, fine-tuning their recognition accuracy for unique fonts, irregular layouts, and complex formats.
The real advantage of ML-enhanced OCR is that it involves significantly less human intervention. Businesses operating in industries such as insurance and finance, where variations between different types of documents are extremely high, can benefit immensely from the adaptive nature of ML models.
Additional Tips for Maximizing OCR Performance
Human-in-the-Loop Validation
While automated OCR is fantastic, sometimes human verification is required, especially for sensitive data extraction activities. Such manual checks as part of your data pipeline ensure higher accuracy in critical documents. It is a “human-in-the-loop” system that allows for the final validation stages before committing any data to business processes.
Invest in OCR Tools with Advanced Error Correction
Some OCR systems also have error correction built in, whereby it detects and corrects minor misinterpretations. Such tools cross-check extracted data against existing templates or business rules to validate the information and correct minor errors.
KlearStack: Your Partner in Optimizing OCR Accuracy
For businesses seeking to take their OCR accuracy to the next level, KlearStack provides a robust solution.
KlearStack is an AI-powered document processing tool with up to 99% accuracy when extracting data from various formats, including PDFs, invoices, KYC documents, and insurance papers. By leveraging machine learning and real-time analytics, We adapt to diverse document types and formats, making it ideal for businesses with high data volumes.
KlearStack’s customizable features allow companies to automate document workflows, improving operational speed while maintaining high data integrity. Whether you need to process complex forms or standard invoices, KlearStack delivers superior OCR performance, ensuring you extract data accurately every time.
Also Read: How To Choose The Right Data Extraction Tool For Your Needs
Conclusion
In the digital age, data accuracy is paramount. For businesses using OCR technology, improving accuracy requires attention to several factors, from high-quality scanning to optimized document layouts and regular software updates.
Employing these best practices will help streamline your data extraction process, reducing manual work and increasing efficiency. With advanced OCR tools like KlearStack, businesses can overcome the limitations of traditional OCR and move towards a more automated, accurate, and scalable future.
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