Optical Character Recognition (OCR) – sometimes called “text recognition” – is a technology businesses can apply to document processing to increase efficiency and reduce costs associated with manual data entry and error correction. Through automated data extraction and storage, complemented by Artificial Intelligence (AI) technology and occasionally some specialized hardware, OCR streamlines the processing of documents to render them in a digital-friendly format with speed, accuracy, and clarity. However, unlike cutting-edge solutions, OCR still requires human oversight because, while helpful, it’s notoriously inaccurate due to myriad inconsistencies within processed documents.
At its most fundamental, OCR automatically captures and stores handwritten, printed, or typed text in a digital format. The source might be a sales order, an invoice, a receipt, a handwritten document, or a photograph of text. The OCR program will scan the source document, identify the individual characters, and convert that information into a machine-readable text file.
Commonly, OCR is used to capture financial, legal, historical, or trade documents so they can be converted to digital text files that can be read by software. In straightforward use cases, OCR can occasionally eliminate the need for humans to manually enter data like text or numerical values into a digital platform like a CRM or ERP.
Since its inception in 1914, OCR technology has grown in usage and sophistication – the global market value was estimated in 2021 at $8.93 billion, with a projected growth rate of 15.4% to 2030 (Grand View Research), and some contemporary methods can recognize multiple languages or handwriting styles.
Despite its widespread usage, however, some have questions or concerns about OCR’s practicality and effectiveness in business. There remain persistent questions surrounding its reliability and real-world accuracy, and certain source formats – Arabic or antiquated calligraphy, for instance – continue to present potential stumbling blocks. With data errors representing a massive risk to companies, does OCR indeed constitute a document processing system that marries efficiency with effectiveness?
Why Do I Hear So Much about OCR?
From the outset, it’s essential to understand the importance of OCR in furthering automation capabilities for businesses working with many documents. Primarily, in terms of business and automation, OCR is a component in the advancement of AI solutions. The future of data-capturing software involves extracting and identifying information using OCR, and analyzing its content via AI tools, resulting in data-capturing software that can not only extract but can comprehend.
The expansion and development of AI capabilities have led organizations to increase their expectations of what is possible in automation. Where OCR was once an impressive technology that nonetheless required the manual supervision of a human, that model is quickly becoming obsolete compared to the merging of AI and OCR.
OCR garners a degree of attention based on its practicality in wider contexts. It has proved valuable in creating systems to assist blind or visually-impaired users and other text-to-speech applications. Additionally, OCR has found a practical application in re-rendering large volumes of antiquated and historical, often unwieldy information into structured, searchable, easily-indexed records.
The History of OCR
The origins of OCR can be traced back to 1914, when the inventor Emanuel Goldberg developed a machine capable of converting characters into telegraph code. Over the next decade, he refined the technology, eventually creating a ‘Statistical Machine,’ the patent for which was bought by IBM in 1931.
In 1974, Ray Kurzweil developed an omni-font optical character recognition system, which he intended to become a reading machine for the blind. Xerox acquired Kurzweil Computer Products Inc. in 1980, which expressed an interest in continuing the advancements of automated character recognition.
During the 1990s and into the 21st Century, owing partly to more sophisticated systems alongside contemporary interest, OCR became a popular method of digitizing historical documents, such as old newspapers. In today’s world, OCR is widely used throughout the public domain – for license plate scanning or translation services, for instance.
How OCR Works
The first stage of OCR processing is for a scanner to physically copy a document, producing a two-tone bitmap which the machine or software analyzes to identify which characters should be read and which areas are background.
Typically, individual characters are processed by one of two algorithms: pattern recognition and feature detection. Pattern recognition involves providing the OCR system with enough examples of a character in various fonts and formats for it to be able to differentiate and recognize those characters when they occur. Feature detection associates specific rules to the unique shape of a character – for example, the letter ‘X’ is stored as two diagonal lines that meet in the center – and uses that logic to identify the content.
OCR technology will also scan the source document to divide it into its structural elements, breaking it down into blocks, paragraphs, sentences, and words.
Zonal OCR is setting specific parameters or margins within a document for the system to scan and analyze – any content beyond those boundaries is not included in processing. Targeting specific areas can reduce the likelihood of errors and optimize the overall process. Full OCR refers to the processing of a complete source document in its entirety – better for capturing complete data but more prone to inaccuracies.
OCR Use Cases
In business terms, the most prominent use case of OCR technology is the extraction and conversion of paper or inconsistently-structured documents into standardized, editable text files, which can be integrated into your other systems of record. For instance, a company might use OCR to process POs or AP invoices. This is one way for businesses to automate data entry.
Using OCR, companies can integrate scanned documents into a big-data system, where the machine-encoded results can be aligned and cross-referenced against various other data sets, such as bank statements and legal contracts. Under this style of data mining, the potential payoff is that the need for a human to manually check and physically enter that data into a centralized system is effectively eliminated.
OCR also presents practical applications in broader society; it may be employed as an aid for the blind and is helpful in situations where quickly scanning a large volume of data is necessary – for instance, passport or license plate recognition.
Issues with OCR
With this all said, OCR is not a fix-all solution to capturing and storing data. It is not perfect technology; business leaders should remain aware that, despite its potential gains, OCR has several inherent weaknesses that can create critical problems.
OCR does not provide an end-to-end solution for data structuring
A primary concern with OCR is that, by itself, it doesn’t fully complete the journey from unstructured to stored, structured data. While it can scan, analyze and translate unstructured data into a machine-encoded format, you’ll need to lean on other machine learning technologies to get that data over the line – or use a member of your human workforce – to accurately move it to the correct place within your system of records.
OCR document scanning is not 100% accurate
OCR comes with the baked-in risk of the system making a mistake when analyzing the structure or format of a source document, irrespective of its sophistication level. Elements such as colored backgrounds, low image quality (for example, blurry or lens-reflected photographs), and skewed orientation affect OCR’s ability to recognize characters accurately.
Certain text-based elements are indecipherable for OCR.
With so much variety in human languages, writing styles, fonts, and character composition, it’s little wonder that OCR will sometimes scan a source document unreliably. It’s been noted that Arabic or Chinese characters, as two examples, present a potential challenge for many OCR systems. This can hurt the operations of businesses that are active or deal with other organizations in those regions. Similarly, there are potential problems with lookalike characters – ‘5’ and ‘s,’ for instance – and those risks are multiplied when you factor in the unique composition of characters in handwritten documents.
Alternatives with Better Accuracy
OCR is an impressive innovation with a storied history, and its current level of precision can be applied practically in a range of situations. It is useful in high-volume character scanning environments, for instance, though it should be said that in those situations, the source material often starts from the point of relative standardization; license plates are typically all stamped in the same font and at the same size.
For businesses, though OCR can be leveraged to good effect, it brings inescapable flaws that can seriously damage operations. TDWI found that most OCR software returns 98-99% accuracy. Although that may initially seem an acceptable margin of error, consider that for a 10,000-character document, it represents up to 200 erroneously processed characters.
Achieving 100% data accuracy is a superior method of gaining pure clarity in your data. Because of that accuracy gap, we only use OCR for documents that begin as images in our data processing; our solution is based on customizable data mapping combined with automated business rules that dictate data integration.
This data accuracy guarantee is one of our core differentiators; if you’d like to find out what Conexiom Platform could do for your business, get in touch with us today.