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Document OCR – The best way to process documents automatically

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For the data entry and extraction, we can take the example of the use of Document OCR in the hospital industry. Hundreds of thousands of data are stored in hospitals as physical papers or digital images daily.

With the use of Document OCR, this data can be readily scanned and converted into more accessible text-based files. Not only it saves data entry time by 90%, but it also improves accuracy.

Document OCR has revolutionized how companies manage and operate their invoicing and billing. We can take the example of Nanonets document processing. It is an AI-based OCR software that can extract data from invoices or bills, purchase orders, or any other document.

Companies can use it to automate their invoicing and billing tasks, and the extracted data can be used to generate invoices much faster.

For the utilization of Document OCR in contract management, we can take the example of an International Bank that needs to automate its contract processes.

When the contracts are stored in paper form, they take up too much space and are equally difficult to access and manage.

By using Document OCR, banks can scan and extract contact information and store them in a cloud-based system, so it is easily accessible as well.

Document OCR can also automate certain aspects of the contracts, as it can quickly notify when some clause on the contract is about to expire or is changed.

A multinational company can utilize Document OCR to efficiently extract and store all employee information from physical documents to a cloud-based system. It can be easily accessed and utilized when needed.

OCR is also widely used in healthcare records management. For example, it allows healthcare providers to convert handwritten notes, lab reports, and other medical documents into digital formats, providing a more efficient and accurate medical record-keeping system.

In legal document processing, OCR technology can save time and improve accuracy. Lawyers can extract data from legal documents and automate contract reviews, saving work hours.

Insurance document management is also a natural fit for OCR. Insurance companies can automate the processing of claims and policies, eliminating the need for manual document sorting and data entry and minimizing errors.

OCR can also support ERP automation. For example, extracting data from invoices or other financial documents can streamline accounting processes and reduce the chance of human error while enhancing business efficiency.

Need any of these solved? Nanonets can do these use cases and more. Solve your document data extraction issues with Nanonets today. Start your free trial.


Challenges of OCR in Document Automation

Poor image quality

For Document OCR to recognize characters accurately and convert them into digital text, the image quality must be clear and distinct. However, sometimes the images that need to be converted are of low quality, which can lead to OCR inaccuracies.

Handwriting recognition

Since handwriting can vary greatly from person to person, OCR engines can often not accurately convert handwritten text to digital text. It can lead to inaccuracies in the final output.

Language recognition

Different languages have different character sets and writing styles. As a result, it can be difficult for OCR engines to identify and convert them into digital text accurately. It is more challenging when the document being scanned contains multiple languages.

Layout and formatting inconsistencies

Different documents may have variations in their layout and formatting, making it difficult for OCR engines to identify and interpret the document’s different segments accurately.

Integration with other systems

For the Document OCR outputs to be utilized effectively in other systems, it needs to be seamlessly integrated with those systems. However, this can be a complex process, especially when dealing with legacy systems that may not be compatible with modern OCR technologies.

Skewed images

Skewed images can cause OCR engines to incorrectly identify characters or segments of text, leading to inaccuracies in the final output.


Best Practices for Efficient Document OCR Processes

To ensure efficient document OCR processes, it is vital to follow certain recommended practices. These practices guarantee higher accuracy rates, faster processing times, and reduced errors, thus leading to increased productivity in the long run. To avoid this, the following points can be considered for an efficient process.

Choosing the right OCR software and hardware

One of the main things is selecting the right OCR software and hardware that suits your specific needs. OCR software applications have different capabilities, features, and limitations.

So, choosing an OCR solution that caters to your business needs, including document volume, type, and format is essential.

Also, choosing the right OCR hardware that can handle the volume of documents to be processed is equally important.

Preparing documents for OCR processing

Preparing the documents prior to OCR processing is another important. It is to ensure that the documents are in the appropriate format, structure, and orientation for the OCR software to recognize the text accurately. This may involve procedures such as scanning, image processing, and file conversion.

Training the OCR system

OCR software requires proper training to recognize and capture the text accurately from the documents. Therefore, training the OCR software through sample documents to recognize different fonts, styles, languages, and formats before full deployment is essential. This training process will enhance the accuracy of results and improve the overall OCR process.

Testing and refining OCR processes

After installing the OCR software, it is important to continuously test and refine the OCR processes to ensure that it delivers the desired results. It involves performance monitoring, error analysis, and fine-tuning of OCR processes to cater to your needs.

Regular reviews of the OCR processes will ensure that the OCR system remains efficient and flexible, thus meeting your evolving business needs.

Integrating OCR with other systems

Integrating the OCR system with existing systems, data repositories, and business processes is crucial to enhancing end-to-end document management. It enables seamless document processing, data extraction, report generation, and analysis, enhancing efficiency and greater productivity.


Have an OCR use case in mind?

Nanonets OCR software is easy to use, set up and provides 95%+ OCR accuracy. Extract data from PDFs, images, emails, and more on autopilot.

Nanonets for Document OCR automation

Nanonets is AI-based online OCR software that extracts texts from images, PDFs, and any other kind of document with 95% accuracy. Nanonets works with all OS and can be integrated with 5000+ apps with easy API and Zapier integrations.

It does not have a desktop app, but it is very lightweight, and therefore, it can be used using any online browser without putting a load on your device.

Nanonets are primarily used to automate all manual data entry processes. So, using Nanonets, you can automate data extraction, document processing, and document verification processes to improve your efficiency.

Nanonets is trusted by 500+ enterprises and over 30,000+ people over the world to extract text from 30 million+ documents every year.

Nanonets customer reviews from ACM, Expartio & Inc2
Nanonets customer reviews from ACM, Expartio & Inc2
Nanonets customer reviews from Ascend, SaltPay and tapi
Nanonets customer reviews from Ascend, SaltPay and tapi

Why choose Nanonets?

  • Easy to use
  • Free plans
  • Free migration assistance – we do the heavy lifting for you.
  • Modern User Interface – Intuitive interface
  • No code platform
  • 5000+ integrations
  • 24×7 support for everyone
  • Exhaustive training material
  • Professional OCR services
  • Cloud and On-premise hosting

Conclusion

The advancements in OCR technology have been revolutionary, making document management much more efficient and cost-effective for organizations. As technology continues to improve, OCR will become even more accurate and capable of recognizing handwriting and even images. The development of machine learning algorithms has already led to significant improvements in OCR, and we are expected to see continued advancements in this area.

Investing in the most up-to-date OCR technology is strongly recommended for organizations considering OCR implementation. Doing so will allow them to achieve the highest levels of accuracy and ensure that the implementation process runs smoothly.

Additionally, it is important to ensure that OCR implementation is aligned with organizational goals and is integrated with existing systems and processes. Organizations should also consider the training required for employees to effectively use OCR and the potential cost savings it can bring.

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