By Jamie Wittenberg
The Challenge: Why Traditional OCR Falls Short
Optical Character Recognition (OCR) has been a key technology in the digital transformation of paper-based workflows. It converts printed or handwritten text into machine-readable data, playing a crucial role in digitizing documents and automating enterprise workflows. However, despite its usefulness, OCR has limitations, especially in terms of accuracy and usability. In this blog, we’ll explore:
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- Three of the challenges with traditional OCR technology
- Why it falls short
- How Hyperscience addresses these shortcomings with more advanced, intelligent solutions.
Three main shortcomings of traditional OCR solutions include:
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- Accuracy limitations
- Data extracted to unstructured text blobs requires further processing downstream to be useful; and
- OCR solutions don’t map neatly to business workflows.
Let’s dive into each of these challenges further.
The Solution: How Hyperscience Overcomes OCR Limitations
Accuracy
Traditional OCR solutions primarily focus on the shape of the character itself to transcribe information from documents. While this works for simple, clear text, it can fall short in various circumstances, such as when characters look similar. For example, the character “0” (zero) and the letter “O” (capital o) are visually similar in many fonts. Traditional OCR systems often misinterpret these characters because they don’t consider the broader context of the text, leading to errors.
In real-world business scenarios, this leads to real-world consequences. For example, if an insurance enrollment or claims system is expecting an exact match for a person’s name, J0hn Smith’s claim might get denied.
The Hyperscience Hypercell moves beyond the basic shape-based recognition of traditional OCR by using sophisticated deep-learning, computer vision, and natural language processing (NLP) techniques to create document classification, field identification, and transcription models that use a variety of factors to identify data in a document and transcribe that data. Instead of focusing solely on character shape, Hyperscience models factor in context and other linguistic elements. This allows the system to more accurately differentiate between a zero and an “O” based on the context in which it appears, such as avoiding the transcription of a zero in the middle of a word.
Structure
Long-form documents such as contracts contain unstructured data. While OCR models may be able to identify all of the characters in a document, they generally lack the intelligence to identify and label individual pieces of information from those dense documents. This limitation forces businesses to manually sift through large volumes of unstructured text to find and understand data.
In addition to character accuracy, the Hyperscience Hypercell excels at identifying and labeling specific information within a document. Instead of returning a blob of unstructured text, the system identifies key fields and pairs them with their corresponding values, making it easier for businesses to extract actionable insights from both structured forms (such as passports) and long-form documents (such as contracts and medical records). With a traditional OCR solution, to make the text usable in a downstream system or a business process, the business often finds itself dedicating hundreds of hours of engineering time to writing regular expressions and rules that convert the blob of text into a usable format. With the Hypercell, text is identified and extracted in key-value pairs, and exported in a format that is ingestible by any downstream system. For example, the output of an insurance claim might look like:
{
first_name: John,
last_name: Smith,
member_id_number: ABC9876,
claim_id_number: 982309,
}
With a traditional OCR solution, a human might need to review the information to discern between the first and last name, and the member ID number claim ID numbers. With Hyperscience, the extracted data is paired with its label. This ensures that the right data makes it into the right part of the next step of the business process.
Workflow Orchestration
Speaking of business processes, enterprises don’t want to just transcribe data – the real value comes from ensuring that data can be used to drive decisions, automate workflows, or integrate with downstream systems. Traditional OCR solutions stop at extraction, often requiring businesses to port the data into another system for further processing, leading to inefficiencies, bottlenecks and additional software costs.
Hyperscience goes beyond simple extraction with our **Flows Engine and Workflow Orchestration capabilities **, which allows enterprises to align data extraction directly with their business processes. Whether it’s for decision-making, case review, or reconciliation, the Hyperscience Flows SDK enables modeling of complex workflows and machine-based error handling, reducing the need for manual intervention and enabling end-to-end automation.
With the advent of LLMs, the Hypercell for GenAI takes all of this one step further, and enables the enterprise to use extracted data in tandem with LLMs. With an OCR solution, you might be submitting an entire document’s worth of text to the LLM as a blob. With the Hypercell for GenAI, the business is in control of which pieces of data get submitted, and additional context is provided to the LLM in the form of the key-value pairs, potentially enhancing the output.
Conclusion
In conclusion, while Optical Character Recognition (OCR) technology has served as a foundational tool for digitizing text, its limitations in accuracy, data structure, and workflow modeling present significant challenges for modern businesses. Traditional OCR often falters in character recognition and struggles with the unstructured nature of extracted text, leading to inefficiencies and errors that can disrupt critical processes. The Hyperscience Hypercell addresses these issues with advanced deep learning, computer vision, and natural language processing techniques, offering superior accuracy and structured data extraction. By modeling business workflows and providing intelligent data output, the Hypercell transforms raw information into actionable insights, significantly enhancing operational efficiency. As businesses continue to demand more from their data processing solutions, the Hyperscience Hypercell represents a pivotal advancement, aligning data extraction with strategic objectives and supporting sophisticated, automated workflows.
While OCR is a useful tool for digitizing paper-based processes, the Hyperscience Hypercell can do so much more. Hyperscience addresses these challenges with more intelligent models that consider context, and it offers a more comprehensive solution by integrating data extraction with business workflows. This makes it not only more accurate but also more aligned with the needs of modern enterprises looking to automate and optimize their processes.
