You’re drowning in a sea of paperwork, and the thought of manually entering all that data into your computer makes you want to curl up in a corner and rock yourself to sleep.
It’s 2023 — shouldn’t there be a better way to handle all those pesky, time-consuming documents?
Optical Character Recognition (OCR) software technology is the modern-day fairy godmother for all the underwriters buried in paperwork. And while it’s not a robot butler (although that’d be cool, too), OCR is just as impressive.
The OCR technology transforms all those intimidating rows of text into editable, searchable files on your computer, saving you precious hours (and sanity.)
What about that crumpled napkin with a handwritten transaction from a mom-and-pop shop? Yep, with OCR, you can edit it on your iOS or Android device.
This technology has allowed finance, banking, healthcare, and other industries to streamline workflows and keep things running smoothly. It’s even making self-driving cars a reality by helping them “read” the road signs!
So, sit back and let us tell you how OCR is changing the game for CRE .
What Is Optical Character Recognition (OCR) Technology?
OCR enables computers to turn printed materials and image files into digital data. The AI-based algorithms used in OCR also enable advanced features like handwriting recognition.
OCR solutions quickly and accurately extract text from PDF documents, scanned phrases, or images. It improves efficiency and productivity by taking manual labor away from your workload.
Optical Character Recognition (OCR) technology transforms how businesses process and manage data. And in an industry that deals with voluminous amounts of data, OCR becomes almost indispensable.
The global OCR market is expected to reach almost 40 million by 2030, growing at a CAGR of 16% from 2022 to 2030. This spectacular growth highlights the increasing adoption of OCR technology across various industries—more so in Commercial Real Estate (CRE).
How Does OCR Technology Work?
OCR technology uses pattern recognition algorithms and artificial intelligence (AI) to convert printed or handwritten documents into machine-readable text formats. It detects characters, numbers, and symbols in a scanned image (i.e., jpg) or PDF file.
OCR technology involves four main stages: image acquisition, pre-processing, text recognition, and post-processing.
1. Image Acquisition
A scanner or a digital camera captures the text from the printed or handwritten source, creating a high-resolution digital image.
Advanced OCR software often uses adaptive thresholding to separate the text from the background in the scanned image. This distinguishes text from other elements in the document, such as images, lines, or background colors.
During the pre-processing phase, the OCR engine processes the digital images to reduce noise and improve image quality.
Image pre-processing involves techniques like de-skewing, binarization, zoning, normalization, despeckling, and script recognition.
- De-skewing involves adjusting the digital image to remove tilt or distortion and fixing misaligned elements.
- Binarization converts the grayscale image into a black-and-white image, enhancing the visibility of the text against the background.
- Zoning separates columns, paragraphs, and individual lines of text.
- Normalization adjusts the size and shape of characters for uniformity, making it easier for the recognition process. The result is a cleaner and sharper image with enhanced readability.
- Despeckling removes spots on images and smooths the edges of text characters.
- Script recognition enables multi-language OCR technology.
3. Text recognition
AI-powered OCR tools identify the characters in the pre-processed image using two principal algorithms—pattern matching and feature extraction:
- Pattern matching compares each character to a predefined set of templates and text examples in various fonts and formats, identifying the best match.
- Feature detection/feature extraction identify each character’s unique attributes, such as lines, curves, and edges. For instance, the capital “H” has two vertical lines connected by a horizontal line across the middle.
Machine learning algorithms and neural networks can even be utilized to increase text recognition accuracy, enabling OCR software to recognize diverse fonts, languages, and even handwritten text.
The final stage of the OCR process deals with converting the recognized characters into electronic documents. The characters are combined to form words, then arranged into sentences and paragraphs.
Advanced OCR systems use a dictionary or library of characters to cross-check the extracted data for errors and ensure higher accuracy.
Post-processing might include spell-checking, punctuation correction, and formatting adjustments to replicate the original document’s layout.
Once complete, the resulting electronic document is searchable, editable, and indexable—ideal for use in various applications and industries.
What Are the Types of OCR Technology?
There are multiple types of OCR technology, each serving a different purpose and catering to different needs.
Optical Character Recognition (OCR)
Optical Character Recognition (OCR) is the most common type of OCR technology. It focuses on converting printed or handwritten text into machine-readable text by analyzing and recognizing individual characters.
Using pattern-matching algorithms, OCR relies on an extensive internal database that contains various fonts and character shapes. It then recognizes characters irrespective of their size, style, or arrangement.
This technology has found widespread use cases:
- Digitizing printed text for electronic devices
- Converting scanned documents into editable digital formats
- Extracting data
- Automatic translation
- Automating data entry for forms and invoices
OCR has its limitations, though. With an endless array of fonts and handwriting styles, capturing everything in a single database is virtually impossible.
OWR Word Recognition
OWR Word Recognition (OWR) is a more targeted method focusing on typewritten text—translated one word at a time. This technique is particularly suitable for languages that clearly separate words with spaces, enabling more accurate recognition and extraction of text.
OWR is a valuable tool for work that requires quick and accurate text processing, such as data entry and document management. Its typical applications include:
- Translating foreign texts
- Improving accessibility for visually impaired users
- Streamlining the transcription of handwritten documents
Optical Mark Recognition (OMR)
Optical Mark Recognition (OMR) is a specialized OCR technology that identifies and processes marks, patterns, symbols, watermarks, and logos on documents.
OMR has several practical applications, including processing forms like surveys, multiple-choice tests, and ballots, where users mark predetermined areas to indicate their choices.
OMR systems are highly accurate and can quickly detect the presence or absence of marks, making them ideal for high-volume data processing tasks.
Intelligent Character Recognition (ICR)
Intelligent Character Recognition (ICR) can read cursive or handwritten notes, all thanks to machine learning and artificial intelligence.
ICR looks at all curves, loops, and lines in handwriting, and turns them into individual characters. It gets smarter and better over time, learning from each new piece of text it encounters.
This tech is a game changer for fields like medicine, law, and finance, where people are always scribbling down significant information. In CRE, ICR can make sense of all those handwritten forms, notes, or even that barely legible legal contract.
OCR vs. AI
OCR is a technology that processes image-based text and converts it into machine-readable text. It uses sophisticated pattern recognition algorithms and machine learning to make this conversion possible.
Artificial intelligence (AI) is an umbrella term for more complex technologies such as machine learning, deep learning, natural language processing, and computer vision. AI applications are far-reaching and can be found in sectors such as autonomous vehicles, robotics, healthcare, customer service, and finance.
While OCR and AI have distinct functions and applications, their combination can unlock powerful potential for enhanced automation and data analysis.
For instance, OCR technology can extract vital data from invoices, contracts, or medical records to feed into AI algorithms that can analyze trends, predict outcomes, and make recommendations. Integrating OCR with AI can facilitate smoother workflows, improve customer experiences, and optimize business operations.
AI can also enhance the accuracy and efficiency of OCR technology. Advanced machine learning algorithms and neural networks can adapt and learn from new data inputs, increasing OCR accuracy even with low-quality images or unconventional fonts.
AI-powered algorithms can improve the accuracy and efficiency of OCR systems by recognizing ambiguous characters or correcting errors based on contextual information.
AI can also dive in and analyze the information OCR pulls out, making sense of all the data and organizing it meaningfully and usefully.
Benefits of Using OCR Technology
The benefits of using OCR technology are wide-ranging, from providing searchable text and improving operational efficiency to increasing data security and information accessibility.
Gone are the days of sifting through piles of paper documents to locate specific information.
Searchable text allows businesses to efficiently arrange, categorize, and manage heavy text-based data, eliminating manual sorting and searching.
Converting paper documents into digital files enables users to search for keywords within a larger database effortlessly. This streamlined process saves time, reduces frustration, and aids in decision-making by facilitating quicker access to essential information.
OCR’s capability to transform handwritten notes into editable digital text enables users to seamlessly compile, revise, and share information, promoting a more efficient and collaborative work environment.
OCR technology’s ability to automatically process and integrate documents into digital workflows reduces the need for manual data entry and document management.
Converting paper documents into digital formats reduces the risk of loss, theft, or damage to sensitive information. Businesses can create secure, encrypted archives of their essential records. These digital archives reduce the risk of data loss due to natural disasters, theft, or accidental damage.
By converting physical documents into electronic formats, OCR technology enables your team to quickly and easily share data with each other—whether physically in the same location or remotely.
Digital documents can also be accessed on various devices, perfect for employees to work on the go.
Additionally, digital documents are often compatible with assistive technologies, ensuring a more inclusive workforce by accommodating the needs of individuals with disabilities.
Blooma + OCR + AI = Streamlining the Pre-Flight Process
The commercial real estate (CRE) market is complex and fast-paced. It leaves underwriters and lenders swamped with paperwork and the copious amount of data necessary to assess and manage their loan portfolios efficiently.
Blooma–plus the use of both OCR and AI technology–has made the pre-flight process a breeze.
The platform harnesses the power of OCR technology and couples it with AI to streamline the pre-flight process, which demands quick decision-making and seamless navigation through complex documentation.
- Transforming Data Extraction and Analysis: OCR technology is designed to extract vital data points from vast arrays of deal documentation. Gone are the days of manually going over and searching for critical information in piles of loan documents, lease agreements, and appraisal reports. With Blooma, the OCR technology rapidly scans documents for essential data, significantly reducing the time and effort spent on data extraction.
- Enriching Insights with External Data Sources: Blooma’s platform doesn’t stop at merely extracting information from documents; it pairs the data captured with relevant external data sources to provide users with powerful insights into their deals. This added layer of external information equips users with a comprehensive understanding of their portfolio and market, allowing them to make faster, better-informed decisions.
- Optimizing Human and Artificial Intelligence: To ensure the highest level of accuracy, every document parsed by OCR technology is processed by an additional layer of AI modeling and validated by a specialized CRE data analyst. This combination of cutting-edge technology and a team of industry experts ensures the highest level of accuracy possible — and in significantly less time.
- Reclaiming Valuable Time and Resources: Blooma’s technology translates to significant time and resource savings for CRE professionals. The platform seamlessly handles data extraction and analysis, allowing users to focus on other critical aspects of loan origination and management, such as building relationships, expanding their portfolio, and growing their business.
As the commercial real estate industry evolves, so should the tools and technology that power it. Embrace the cutting-edge technology in Blooma’s platform and revolutionize your pre-flight underwriting process for CRE loans and management.
Request a demo today and discover how Blooma can elevate your CRE lending strategy to new heights.