In this article, we will explore exactly what an OCR model is, how it works and some of its benefits. Read on to learn more.
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An OCR (Optical Character Recognition) model is a tool that reads and converts text from images or scanned documents into editable, digital text. It’s like teaching a computer to recognize letters and words in pictures or handwriting.
Example: An example of an OCR model is Google Lens, which can extract text from a photo of a product label, such as "Wireless Keyboard" or a serial number like "123-456789," and make it searchable or editable on your device.
Here are some of the benefits of using an OCR model:
OCR models eliminate manual data entry by quickly extracting text from invoices, receipts, or contracts. This saves time and reduces errors, allowing businesses to process documents more efficiently.
OCR converts scanned documents into searchable text, making it easier to locate specific information. This improves productivity, especially in industries with large document databases.
OCR simplifies processes like scanning ID cards or forms for new customers. This speeds up onboarding while ensuring accurate information capture.
By digitizing physical documents, OCR reduces the need for bulky file storage. Businesses can save space and access important records digitally.
OCR enables screen readers to interpret text from images or scanned files, improving accessibility for visually impaired users. This promotes inclusivity and compliance with accessibility standards.
Use our 10-step OCR model process to efficiently read-out your files:
The first step in OCR is preparing the image by enhancing its quality, like adjusting brightness or removing noise. This ensures that the text is clear and easy for the model to interpret.
Example: Before scanning a receipt for a laptop purchase, the image is processed to remove blur, making the serial number "123456ABC" more legible for extraction.
The OCR model detects areas in the image where text is likely present. This helps the model focus on relevant sections for further processing.
Example: When scanning a product label on a smartphone, the OCR model identifies the text region containing the model number "SM-1234XYZ."
The model separates detected text into individual characters to understand each letter or symbol. This step is crucial for accurately recognizing each character.
Example: The model segments the word “Printer” on a product box, breaking it into characters like “P,” “r,” “i,” “n,” “t,” “e,” and “r.”
The OCR model uses machine learning algorithms to recognize each segmented character and convert it into a digital format. This is where the actual text recognition takes place.
Example: The model reads the serial number “LM-987654” from a laptop and converts it into editable text.
To increase accuracy, the model uses context to verify character recognition, such as predicting a word based on surrounding characters. This helps fix misrecognized characters.
Example: When reading "Wireless Mouse," the model uses the context to confirm that "Mouse" follows "Wireless" instead of misreading it.
After the text is recognized, the OCR model checks for errors and corrects common issues like misinterpretations of characters. This step improves the overall accuracy of the result.
Example: The OCR model scans an invoice for a tablet and corrects a misrecognized “I” as “1,” ensuring the product ID is accurately read.
Once the text is recognized, the model arranges it in a format that matches the original document’s layout. This can include adjusting spacing and alignment.
Example: After scanning a warranty document, the OCR model formats the serial number “ABCD1234” exactly where it was located on the original document.
The model converts the recognized text into a usable digital format like a Word document, PDF, or CSV file. This allows the text to be easily edited or stored.
Example: After scanning the receipt for a pair of headphones, the OCR model outputs the total price “$150” into an Excel sheet for further processing.
Finally, the output is manually or automatically verified to ensure the text is accurate and usable. This step ensures the OCR results are reliable for business use.
Example: After scanning a product barcode for a set of speakers, the data is cross-checked with the company's inventory system to ensure the model number and price match.
OCR data can be integrated into business systems like CRM, ERP, or databases for seamless processing and storage. This ensures that extracted data can be easily accessed and used across various platforms.
Example: The OCR model scans a contract for a computer and automatically uploads the serial number to the company's database for inventory tracking.
TechRetail Solutions is an e-commerce company that processes thousands of receipts, invoices, and warranty documents daily. To improve efficiency, they implement an OCR model following our structured 10-step process. Here's how:
A customer uploads a receipt for a laptop purchase, but it's slightly blurred. The OCR system sharpens the image to make the serial number “123456ABC” readable.
Once the image is processed, the OCR system identifies the sections containing relevant text, such as the store name, transaction date, and product details. It accurately pinpoints the serial number and product model, filtering out unnecessary elements like background patterns and logos.
The OCR system then breaks down the detected text into individual characters. For the product name “TechLaptop X100,” each letter and number is isolated to prevent misinterpretation, ensuring that no part of the text blends together.
After segmentation, the OCR model matches each character with its corresponding digital equivalent. The system correctly identifies and converts “X100” into text, preventing any mix-up between similar-looking characters like “O” and “0.”
During recognition, the system verifies the extracted text using context. If it initially detects “X1OO” instead of “X100,” it cross-checks the surrounding text and corrects the mistake, ensuring that the final output matches the intended product name likely.
Before finalizing the extraction, the OCR system runs an error check. In one instance, it mistakenly reads a serial number as “LM-987S54” instead of “LM-987654.” The built-in error correction mechanism identifies the issue and replaces the incorrect character with the correct one.
Once the text is accurately recognized, the system arranges it in a structured format. The extracted serial number and product model are placed under the “Product Details” section in the same alignment as the original receipt to maintain consistency.
The final processed text is then stored in a structured format for further use. The warranty claim details, including the purchase date, serial number, and product model, are exported to a CSV file, making the data easy to manage..
Before processing the warranty claim, the extracted details are automatically verified against TechRetail’s internal database. The system confirms that the serial number “123456ABC” matches the company’s records, ensuring that the claim is valid.onfirm the purchase details match.
After verification, the extracted data is integrated into TechRetail’s CRM and warranty management system. The system updates the customer’s profile, processes the claim, and automatically sends a confirmation email stating that the warranty request has been successfully registered.
We hope you now have a better understanding of how an OCR model works and its benefits. If you enjoyed this article, you might also like our article on OCR document classification or our article on OCR NLP.