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In this article we’ll overview the most used applications of OCR, compare it with AI-powered solutions and help you choose the right fit for your business goals.
What Is Traditional OCR?
Optical Character Recognition (OCR) refers to the mechanical or electronic conversion of texts from mostly paper carriers into editable digital data. Necessary information can be extracted from digital camera images, PDFs and scanned documents such as invoices, ID scans, or receipts.
In due course, OCR was a technological breakthrough implemented across various industries for tasks such as digitizing paper records, automating data extraction from invoices and reports, and streamlining document management. However, the process was often time-consuming and brought inaccurate results.
As traditional OCR technology works by analyzing patterns and matching them to a predefined database of characters, any deviation in fonts or spacing is able to confuse the system. While effective for clear, well-structured documents, it struggles with complex fonts, poor-quality scans, or handwriting. It requires precise formatting and cannot adapt to variations in text appearance. However, even with these limitations, traditional OCR solutions were (and still are) a great assistance to any business, helping save hours of manual labor.
How OCR Works?
To understand how OCR technology works, here is a step-by-step breakdown of actions taken to convert a document, image or photograph containing textual information into the desired text:
Step 1: Image Capture
A physical document is scanned to get a raw digital file.
Step 2: File preprocessing
It is cleaned up by removing noise, adjusting brightness, making the text clearer, or straightening the image if it’s skewed.
Step 3: Text detection
The OCR software identifies the text in the file. It separates the text areas from non-text parts (like images or graphics).
Step 4: Character segmentation
The document is divided into individual lines, words, and characters. The software identifies the space between characters, making sure to separate them.
Step 5: Character recognition
It compares each segmented character to stored visual patterns to match the most likely letter, number, or symbol.
Step 6: Postprocessing
After the characters are identified, the system checks for any potential errors and corrects them.
Step 7: Output
The recognized text is converted into a machine-readable format.
What is traditional OCR used for?
OCR is widely used to automate data extraction. Below are some industry cases of its use in daily operations:
- Healthcare: it helps convert handwritten patient records into digital formats for easy access and analysis.
- Customer support: scanning and organizing customer queries or documents for faster responses.
- Retail: simplifies inventory management, scanning product barcodes, and processing invoices.
- HR and rerouting: automated processing of resumes, applications, and employee records.
- Legal industry: used to digitize contracts, case files, and court documents, streamlining research and legal document management.
- Transportation: license plate recognition, speeding up toll collection, and vehicle tracking by processing images from cameras.
- Banking: OCR services are used to process checks and forms for automatic data entry.
- Real estate: uses optical character recognition to digitize property documents and contracts, making transactions smoother and faster.
These applications enhance operational efficiency in each sector, and they can be tailored for the use of a particular business, making them indispensable in the current business world.
What is AI-Powered OCR?
AI-powered OCR is the next gen of optical character recognition that integrates machine learning to constantly improve. Unlike the traditional system, which relies on predefined templates, AI-driven tools can recognize a wide range of fonts, complex layouts, and handwriting, and work with sources of low quality.
Key features include:
- Context understanding with the help of Natural language processing (NLP);
- Image preprocessing for noise reduction;
- Self-learning capabilities that improve over time;
- Multi-language support;
- Automated data extraction to identify and categorize key information without manual input.
Deep learning, neural networks and Computer Vision techniques enable systems to handle different types of documents and extract data with higher accuracy even from low quality scans or handwritten text. That’s a lot of automation to save time and manual labor.
Leveraging GenAI in OCR Systems
Generative AI (GenAI) refers to a category of artificial intelligence technologies focused on generating new content, data, or solutions based on learned patterns. In OCR, GenAI-based systems utilize generative models to enhance the recognition and interpretation of text, especially in complex scenarios.
While AI-driven model rely on pattern recognition and is highly effective for structured documents and standard fonts, custom Generative AI solutions use generative models and deep neural networks like generative adversarial networks (GANs) or transformer-based language models to create text from input data. They can generate text that fits into the context, even if the image quality is poor or if the handwriting is not clear.
What is AI-powered OCR used for?
Every business needs an accurate document flow, and with the enhanced ability to extract text from any source, AI-driven systems are indispensable for the task.
- AI in healthcare analyzes patient data, detects diseases or anomalies through image recognition and provides real-time recommendations on treatment.
- In customer support, scanning feedback for insights and streamlining ticket processing are made easier.
- Retail businesses are able to extract detailed information from receipts, invoices, and old house titles with more precision. AI enhancement can also help with inventory optimization and product demand forecasting.
- HR efficiently is backed up by processing employee records, resumes, and contracts.
- The legal industry benefits from simplifying document review and predicting case outcomes.
- Transportation and logistics are improved by easier and faster vehicle document scanning and license plate recognition.
- The banking industry improves fraud detection with accurate data extraction from forms,checks, and bank statements.
💼 Real-Life Case: Breg, a U.S. based orthopedic bracing company implemented an Optical Character Recognition (OCR) engine to extract printed text from scanned patient documents. This automated the claims process, reduced response times and manual data entry costs. As a result, Breg enhanced operational efficiency, allowing staff to focus on higher-value tasks and improving overall service quality.
In each of these areas, AI delivers more precise results, reducing human error and increasing efficiency.
Traditional OCR and AI-Driven OCR Comparison
Now that we’ve learned about two variations of OCR models, let’s take a deeper look at their crucial differences that might help in choosing the right system for business needs.
OCR Benefits
Though traditional OCR software seems outdated nowadays, for many tasks it is still efficient enough. Let’s check the advantages of traditional model:
- Time efficiency. Fast text extraction and processing large volumes of data in comparison with manual work.
- Cost-efficiency. Reduced for manual labor and lower operational costs. This is particularly significant in industries that require the constant processing of paper-based documents.
- Improved data accessibility. The extracted text is easily searchable, editable, and storable in digital formats.
OCR Limitations
While traditional OCR technology offers numerous benefits, it also has certain limitations:
- Accuracy issues with poor-quality sources. Traditional OCR’s efficiency depends on the quality of the original document. Low-resolution scans, blurred text, or complex fonts can lead to errors in text recognition.
- Language and font limitations. OCR systems struggle with recognizing certain languages, handwritten text, or unusual fonts.
- High setup costs for advanced features. Advanced OCR systems can be costly to set up and maintain.
AI-Driven OCR Benefits
AI-driven OCR technology uses an advanced approach to optical character recognition, leveraging the power of machine learning. Here are the key advantages:
- Better adaptability. These systems continuously learn and adapt, improving their performance over time.
- Higher accuracy. AI-driven OCR systems are good for automated data capture and can recognize text with higher accuracy, including handwriting, difficult fonts, and poor-quality scans.
- Enhanced text recognition. It can recognize and extract text from a wider range of sources, including scanned documents, images, PDFs, and forms with greater flexibility.
- Contextual understanding. AI-based OCR can understand the context of the text, allowing for better interpretation of data.
- Automation and speed. AI types of OCR are faster and more accurate in data processing, reducing the need for manual checks and speeding up workflows significantly.
AI-Driven OCR Limitations
While AI-driven OCR offers significant advantages, there are also limitations that businesses need to consider:
- High Initial cost. AI-driven OCR systems require a significant upfront investment in terms of both financial resources and training to ensure optimal performance.
- Training and tuning. These systems require ongoing training and tuning to stay relevant. Continuous adjustments are needed to handle specific use cases effectively. Also, if the training datasets didn’t contain a wide range of fonts, document types or languages, the real-world performance would be unsatisfactory.
- Complexity in setup. Implementing AI-driven OCR requires specialized knowledge and expertise for setup, integration, and maintenance. The operation of such systems also requires higher computational power requirements that are not always accessible for smaller companies due to high costs.
- Overfitting risks. If not properly trained, AI technology might become overfitted to specific datasets, leading to poor performance when processing new or diverse types of documents.
So Which Solution Works Better For Your Business?
When choosing between AI and OCR of the traditional type, the decision depends on the specific business task at hand.
Traditional optical character recognition technology is ideal for processing clearly printed or well-formatted documents. Its strength lies in speed and efficiency with simple, structured data extraction, such as invoices, receipts, and printed forms (of good quality).
AI-driven OCR is perfect when dealing with more complex documents, such as handwritten text, poorly scanned forms, or documents with varied layouts. The integration of machine learning enables these tools to adapt, learn, and improve, recognizing patterns, handwriting, and non-standard fonts with higher reliability.
5 Simple Questions To Help You Choose
To choose between traditional system and AI-driven one correctly, answer these simple questions:
- What types of documents do you process (printed, handwritten, or both)?
- What is the volume of documents you handle daily?
- How important is accuracy in data extraction?
- Does your business need to handle documents in multiple languages or varying formats?
- What is your budget for OCR technology?
The higher the workload and the broader the goals, the higher the chances AI-driven OCR is your match.
Why Choose Datavise For AI-Powered Solutions?
We provide modern businesses with tailored up-to-date AI-driven tools that will fit perfectly in your current workflow. The main reasons to work with us are the following:
- We help fast-growing companies. The modern business world is highly dynamic, demanding adaptable tools for ever-evolving circumstances. We offer solutions that will grow with the business, meeting new challenges effortlessly.
- We adapt to your use case and your budget. With a deep understanding of business needs, we deliver tailored solutions that align seamlessly with our clients' goals.
- We provide valuable insights to help you stand out. By using advanced analytics, machine learning models, and data-driven strategies we help you optimize costs, enhance decision-making, and gain a competitive edge in your industry.
- We care about data protection. We prioritize the security of sensitive information, ethical AI use, and ensure full privacy measures.
Ready to unlock the power of AI for your business? Contact us today to transform data into actionable insights!
