What Is OCR and How Businesses Leverage It in 2023?

There is a vast amount of data, most of which is embedded within images and documents. Unfortunately, ordinary word or text processing software often falls short when it comes to extracting valuable information from these visual formats. Optical character recognition (OCR) emerges as an incredible solution to this problem. OCR opens up a world of possibilities by converting printed text within images into editable and searchable digital text. By integrating OCR engines with other AI technologies, the solutions can even turn images into structured data that can be directly imported into other software for analysis or automation.

This transformative technology bridges the gap between visual and textual information and comes with many benefits. Streamlining data entry, enhancing searchability, improving data accuracy, and improving data accessibility are some of its key advantages.

In this blog post, we will discuss what OCR is, how it’s different from Intelligent Document Processing, why you should use it, and how businesses are leveraging OCR.

Optical Character Recognition (OCR) is a technology that recognizes and converts text contained within images or scanned documents into machine-readable text. Bridging the gap between the world of printed or unstructured documents and text processing, it's a landmark in modern data processing technology. It analyzes characters' shapes, patterns, and configurations within an image, interpreting them as actual letters, symbols, or numbers.

Moving further, it transcribes those letters or symbols into editable and searchable text format, making extracting useful information easy. All in all, Optical character recognition automates data entry, digitizes paper-based documents, and enhances document searchability. As more businesses are moving from It's helpful in industries such as finance, healthcare, and information management.

Most people use the terms OCR and IDP interchangeably, but they are not the same. OCR converts text within images, or scanned documents into machine-readable texts. These texts aren’t organized or structured into rows and columns. The results still need to be manually processed into tabular form or other structured formats in order for other software or application to make use of them.

Conversely, Intelligent Document Processing (IDP)is a technology combining Optical character recognition with other advanced AI capabilities, such as natural language processing (NLP), machine learning (ML), and large language models (LLM)s, to convert images or documents into structured data formats like CSV, XML, or JSON. It automates extracting, interpreting, and processing information from unstructured data sources. However, many still use the term “OCR software” to refer to Intelligent Document Processing solution.

Optical character recognition plays a crucial role in automating data extraction and more businesses are integrating it into their systems to make data processing efficient. As per Globe Newswire, the OCR market is expected to grow at a CAGR of around 13.2% between 2023 and 2032. Here are the many benefits your business can enjoy by integrating OCR into your data systems:

Enhanced Efficiency and Productivity

Optical character recognition technology is a potent catalyst for boosting business efficiency and productivity. It automates text and data extraction, with the help of other AI technologies, from diverse documents like invoices, forms, and contracts, reducing the need for time-consuming manual data entry.

This automation slashes the risk of human errors, as 75 percent of data loss is caused by human error, and liberates employees from repetitive tasks. Therefore, employees can concentrate on strategic and value-added activities. Industries, such as finance that rely on swift document processing for billing and financial reporting can particularly benefit from this error-free data capture of OCR.

Better Accessibility and Searchability of Information

OCR's transformative role lies in converting paper-based or image-based documents into digital text that is both searchable and editable. This conversion fosters accessibility and searchability, fundamentally changing how businesses handle information.

It also empowers companies to create searchable archives of historical documents, simplifying the retrieval of specific information when needed. Moreover, Optical character recognition technology aids customer service by quickly retrieving customer data from scanned documents. This leads to quicker response times, shorter onboarding process, and higher customer satisfaction.

Cost Reduction

OCR can significantly reduce operational costs for businesses. Automating data entry and document processing tasks minimizes the need for manual labor, reducing the associated labor costs.

Furthermore, Optical character recognition can streamline workflow processes, reducing the time and effort required to handle documents. This cost-saving aspect extends to physical storage space, as digitized documents eliminate the need for extensive paper archives, which can be expensive and space-consuming.

Enhanced Security

Optical character recognition technology contributes to better security and compliance within businesses. It digitizes and stores documents electronically, reducing the risk of physical document loss, theft, or damage.

In addition, it allows for the implementation of robust access controls and encryption measures to safeguard sensitive information. In sectors like finance and healthcare, where data security and regulatory compliance are paramount, OCR ensures that documents are handled securely, with restricted access to authorized personnel only.

Optical character recognition engines undergo several steps to convert images into texts. Let’s take a look at them.

Step 1: Pre-Processing

Pre-processing is the initial phase of OCR software. It plays a vital role in preparing the input image for accurate character recognition. This stage involves image enhancement and transformation techniques to optimize the quality and readability of the document being processed. Here are some of them:

• De-skew: In this step, OCR software corrects any skew or slant in the document caused by scanning or image capture. It ensures that text lines are aligned horizontally.
• Binarization: The image is converted into a binary format, where pixels are categorized as either black (foreground) or white (background). This step enhances the contrast between the text and the page.
• Despeckle: Small noises or speckles in the binary image are removed to clean up the document and improve character recognition accuracy.
• Line Removal: Lines and graphical elements not part of the text (e.g., ruled lines or decorative marks) are eliminated to isolate the text content.
• Zoning: The document is divided into distinct zones or regions to identify areas of interest, such as headers, footers, or body text. This zoning helps in better character recognition.
• Script Recognition: For multilingual documents, OCR may perform script recognition to identify the script or writing system used (e.g., Latin script, Cyrillic script) to improve character recognition accuracy.
• Segmentation: The document is divided into individual characters or words, separating them for further analysis.

Step 2: Character Recognition

The next step is character recognition, which is the core of Optical character recognition, where the system identifies and interprets individual characters within the segmented text regions. This involves a careful analysis of the patterns and features of characters to convert them into machine-readable text. Here are the steps involved:

• Matrix Matching: In this, OCR software compares the shapes of the segmented characters with a predefined library of character shapes. It attempts to match the extracted character patterns with known characters, enabling text recognition.
• Feature Extraction: Alongside matrix matching, OCR extracts various features of characters. These features include stroke patterns, lines, angles, proportions, and other distinguishing characteristics. This plays a crucial role in character discrimination and enhancing recognition accuracy.

Step 3: Post-Processing

Post-processing in OCR is a vital phase where recognized text is refined to enhance accuracy and usability. OCR software reviews the text for errors and inconsistencies, applying context and language models to make corrections. It also ensures that the OCR output mirrors the formatting and styling of the source document, preserving font styles and layout. The outcome is a machine-readable text document that can be easily searched, edited, and processed digitally.

OCR, while a powerful tool, is not without its limitations.

Firstly, it struggles when dealing with text from sources that use a font size smaller than 12 points, often resulting in more errors in recognition. Additionally, during the text scanning process, most document formatting is lost, meaning that the end results most likely won’t have bold or italicized texts.

The biggest limitation of OCR engines is that OCR alone isn’t capable of turning the extracted texts into structured formats. It needs other AI technologies as we discussed above in order to perform intelligent data extraction, which enables businesses to automatically classify and extract data from all kinds of documents regardless of their layouts.

Some of the tips that help you in improving the accuracy of Optical character recognition are:

High-Quality Source Images

The foundation of accurate OCR lies in the quality of the source images. Start with high-resolution, well-lit, and sharp images to ensure optimal results. Avoid photos with blurriness, distortions, or low contrast, as these factors can significantly hinder character recognition. Scanning documents at a high DPI (dots per inch) or using high-quality camera settings for image capture can make a substantial difference in OCR accuracy.

Powerful OCR Software

Selecting the right OCR software is also very crucial. Opt for powerful OCR tools with advanced features and robust recognition algorithms. Top-tier OCR software can handle various fonts, languages, and document layouts, leading to more accurate results. Consider software that provides post-processing options for error correction and formatting to refine the OCR output and even enable automation of various workflow.

Go for Right Size Images

Another thing that matters for OCR accuracy is image size. Aim to capture or scan documents at an appropriate size that maintains text legibility. Images that are too small may result in character recognition errors, while oversized images can slow down the OCR process and potentially introduce inaccuracies. Striking the right balance by ensuring that text is clear and properly scaled in the images will enhance OCR performance.

Now that you are aware of what it is and how it works let's explore its use cases:

• Invoice Processing: Processing invoices can be hectic since they can come in various formats. When executed manually, invoice processing time-consuming and error-prone, leading to inefficiencies and delays in payment processing. With invoice OCR, the accounting and financial department can automate extracting relevant data, such as vendor details, amounts, due dates, etc., from invoices regardless of the layouts and integrate it with other applications like AP or ERP software to automate the accounts payable process.
• Receipt Scanning: OCR extracts and organizes receipts for personal or business financial management. Receipts are important documents involved in a lot of internal processes like three-way matching and expense reimbursement. Moreover, receipts contain important consumer behavior data that can be analyzed for businesses to come up with more personalized marketing campaigns or product recommendations. With receipt OCR, businesses can automatically extract data fields like merchant name, product info, total amount, etc., to automate receipt processing.
• Automated Forms Processing: Processing various forms, such as applications, insurance claims, and tax forms, for quicker data retrieval and analysis enables businesses to expedite customer onboarding process and provide better customer service.
• ID Verification: When onboarding new employees or clients, businesses will often need to collect information such as their full name, date of birth, and address. This data can be extracted from a scanned ID document.
• Survey Analysis: Analyzing and extracting data from paper-based surveys and questionnaires for research and feedback collection.

Optical character recognition is a transformative technology with various applications and benefits in the modern world. Many automated workflow need data input but more than 80% of data remain unstructured. OCR, combined with other AI technologies, converts images or scanned documents into machine-readable formats, improves data entry efficiency, enhances accessibility, and enable businesses to gain insights from vast volumes of data.

FormX is an Intelligent Document Processing solutions that combine OCR, ML, NLP, and LLMs like GPT-4 to help businesses extract data from any kinds of documents. It comes with a set of pre-built data extraction models, or extractors, including receipt, invoice, bill of lading, and more so that businesses can begin automate data extraction with minimal setup time. Moreover, training a new extractor requires as little as one sample and a few minutes and integrating FormX with with other software can be easily done via API to form an automated workflow.

Contact us now today to learn more about FormX and how it can be of help to your business.