Major limitations Of OCR technology and how IDP systems overcome them

Optical Character Recognition (OCR) technology, also called text recognition, converts extracted data from documents, PDFs, and images into machine-readable and editable texts. Companies use OCR technology to reduce, if not eliminate, manual data entry from their workflows. 

However, traditional OCR software is limited by the technology of its time. Originally, the software was designed to extract text from black-and-white printed documents. But, with time, documents, IDs, and legal papers became more diverse and colorful, making it more challenging to process them. 

That’s not all; there are many more drawbacks to using simple OCR technology, such as lower accuracy rate, limited language support, resource intensiveness, and lack of contextual understanding, among a few others. 

To understand the limitations of OCR technology and how advanced applications like Intelligent Document Processing (IDP) overcome them, keep reading. 

Limitations of OCR technology 

More often than not, OCR software extracts data from image-based documents. However, the uptick in scanned documents with different formats, fonts, styles, and colors has led to several OCR limitations that include: 

1. Accuracy

Generally, advanced OCR software has an accuracy rate of 99%, provided the input is a high-quality, black-and-white image with large fonts. 

However, the accuracy rate is often compromised when the document processing software deals with handwritten content, intricate layouts, or skewed texts. OCR software also generates incorrect readings from minuscule texts and low-quality images. These inaccuracies affect the extracted data’s overall quality and integrity. 

2. Language and font support

The optical character resolution platform uses pattern recognition algorithms to match the scanned texts and characters with the ones present in its database. Naturally, the system generates inaccurate readings when it encounters fonts or languages that deviate from its pre-fixed parameters. For instance, if someone generates a cool font using an online font generator tool, the system could fail to understand it.

Since the algorithm is not adaptive, it may fail to identify unique language symbols or misinterpret certain characters. Consequently, organizations struggle to use OCR technology for effective multilingual and diverse document processing. 

3. Formatting errors

Simple optical character recognition applications do not preserve the document’s original formatting. Without third-party software aids, OCR technology struggles to process line breaks, font styles, indentations, tables, and graphs. 

As a result, the newly generated document contains misaligned text, erroneous spacing, incorrect line breaks, and incomprehensible tables. These formatting errors are manually ironed out by the employees, decreasing productivity and adding unnecessary load to the workforce. 

4. Sensitive information exposure

OCR software’s parameter-based data extraction technique might inadvertently upload sensitive information, such as IDs, confidential documents, and financial data to the software provider’s server. The data then becomes vulnerable to cyberattacks and security breaches. It becomes essential to mask and redact such information before putting the documents through the scanner.

Furthermore, companies need to ensure proper storage and utilization of this sensitive information as per GDPR and SOC-2 guidelines. Any irresponsible handling of these confidential documents warrants harsh penalties. For instance, Meta (formerly Facebook) was fined a staggering $1.3 billion for violating GDPR norms in Europe. 

5. Dependency on image quality

OCR software’s performance hinges on the quality of the source images or documents. Low-resolution images, faded text, or poor lighting conditions can introduce noise that hinders accurate character recognition. Blurry or distorted images might cause the software to misinterpret characters, leading to transcription errors and requiring manual intervention to rectify discrepancies.

6. Time and resource intensive

Integrating OCR software with your existing business stack is no easy task. It requires significant investments in time, capital, and human resources. The integration process starts with configuring the software to work seamlessly with your legacy systems. 

Since it requires high-quality images to generate the best results, you would need to invest in ancillary applications to improve the input quality. If the organization is processing documents with a unique formatting style, additional capital will be required to develop the custom pattern recognition algorithm.

Lastly, the business needs to create a training module to teach the employees about the newly integrated system. 

7. Learning curve

The OCR training module usually covers everything from pre-processing the images to ethical storage of the extracted information. However, gaining operational proficiency requires time. The adoption process of the newly implemented system might be slow as employees come to grips with the user interface, troubleshooting methods, and configuring parameters for pattern recognition. 

Subsequently, the company also needs to account for the opportunity cost lost due to the steep learning curve and slow adoption rate of OCR software. 

8. Lack of contextual and semantic understanding

Simple OCR software lacks any machine learning or natural language processing algorithms. Therefore, while it excels at character recognition, it fails to understand the nuances of a text passage and the relationships between the extracted data. 

This limitation is most noticeable when companies try to create interconnected knowledge repositories using only OCR platforms. In doing so, they can only establish connections using keywords, not the search intent of the user.

9. Restricted application domains 

Even though the OCR system is versatile, its limitations prevent widespread adoption in all major industries. For example, this system struggles with handwritten drawings, special equations, and complex scientific symbols.

Alternatives to OCR technology 

The question is, is there any technology to counter the OCR disadvantages we discussed above? Fortunately, there are 3 OCR alternatives currently leading the market. 

1. Intelligent character recognition (ICR)

ICR, or Intelligent OCR technology, combines the adaptability of ML algorithms with the data extraction ability of the OCR platform to create software that excels at extracting and interpreting data from handwritten documents. Apart from that, it shares all the other perks and features of regular OCR technology.

2. Computer vision

Computer vision uses artificial intelligence and cameras to mimic the abilities of OCR software. In fact, the integration of AI makes it better at extracting information from various visual mediums, such as images, videos, digital documents, and any other inputs. The AI also continuously improves its data extraction techniques to maximize productivity while minimizing human intervention. 

3. Intelligent document processing (IDP)

Intelligent document processing refers to the end-to-end automation of data extraction from paper-based as well as digital documents by using OCR, NLP, ML, and AI technologies. 

What’s the difference between IDP and OCR technology? 

Intelligent Document Processing (IDP) software transcends Optical Character Recognition (OCR) solutions by encompassing a broader scope of capabilities. While the OCR platform adeptly converts text from documents, IDP software integrates OCR with advanced AI algorithms. This combination not only transforms extracted data from documents, PDFs, and images into machine-readable texts but also understands context, semantics, and relationships within the content. 

It enables IDP software to interpret data meaningfully and perform intricate tasks like categorization, data validation, and decision-making.

Unlike traditional OCR technology, which operates on a character-by-character basis, the IDP platform processes information holistically, ensuring accurate context awareness. It overcomes OCR's limitations, like language barriers and formatting errors, by deploying adaptive algorithms. This equips organizations with more efficient and precise data processing, reducing manual intervention, enhancing data accuracy, and unlocking insights that simple optical character resolution technology cannot provide.

Why are organizations upgrading their OCR technology to IDP solutions? 

Businesses are looking to automate business processes that rely on manual input and intervention. IDP platforms effectively extract data and structure information so that it can then be processed further or sent to downstream applications. Other benefits include: 

1. Improved accuracy 

Post the implementation of IDP solutions, businesses can see their data accuracy rate increase up to 99.9%, along with a high STP rate of 95%.

2. Faster document processing 

Structured, unstructured, and semi-structured are converted into usable structured information. The result is faster document processing with end-to-end automation for document-dependent business processes. 

3. Better document storage

As the data is stored in the cloud, organizations move to a paperless storage environment. 

4. High compatibility

Most IDP platforms come with native integrations for leading business ERPs. Furthermore, these platforms offer multiple pre-trained APIs to help companies connect business systems not available on the native integration lists. 

Applications of IDP systems in different industries 

IDP platforms automate document processing across several industries, such as: 

1. Banking 

Using IDP software, banking institutions automate the extraction of critical information from a wide range of documents, including checks, bank statements, invoices, and forms. This automation eliminates the need for manual data entry, minimizing the potential for errors that can arise from manual data entry.

Moreover, the ability to swiftly and accurately convert printed text from these documents into machine-readable improves searchability and archiving ability. This not only enhances the customer experience by expediting processes but also empowers banks to maintain a higher degree of accuracy in their financial operations.

2. Insurance

Insurance companies can streamline the extraction of relevant data from a wide range of structured, unstructured, and semi-structured documents, such as policy documents, ACORD forms, claim forms, and correspondence. Automation has multi-fold benefits: it speeds up claims processing time, enables insurance providers to quickly respond to customer needs, and improves record-keeping workflows. 

3. Logistics

Shipping labels, invoices, and custom documents are essential components of logistics operations. An intelligent document processing system quickly extracts relevant data from these documents at scale, reduces the risk of manual errors, and improves tracking accuracy. 

4. Real Estate

Property transactions involve a plethora of documents, including contracts, deeds, leases, and rental agreements. The IDP technology facilitates the digitization of these paper-based documents, directly improving storage, retrieval, and document management processes. Owing to the increased searchability of these documents, real estate companies can improve their due diligence processes and dispatch the paperwork faster. 

Conclusion

Docsumo, an AI-powered IPD platform, uses OCR technology with advanced functionalities to automate document processing. 

• For starters, it uses an extensive pre-processing system to improve the image quality before passing it through the OCR platform. 
• Following the data extraction process, the information is instantly validated and verified. 
• Finally, the information is categorized and archived for better document management. 

In other words, Docsumo overcomes the traditional OCR limitations using contemporary technologies, like AI, NLP, and ML, while being GDPR and SOC-2 compliant. 

Try Docsumo’s advanced OCR with their 14-day free trial.