Text recognition with Computer Vision
Recognition of the text on the label
Text recognition with computer vision is the process of automatically identifying and extracting text from images or video using algorithms and machine learning techniques. This technology can be used to extract text from scanned documents, product labels, license plates, and more.
The process typically involves the following steps:
Image preprocessing: The image is prepared for analysis by performing operations such as noise reduction, image scaling, and thresholding.
Text detection: The computer vision algorithm scans the image and identifies areas that may contain text.
Character segmentation: The algorithm separates the individual characters within the detected text areas.
Optical character recognition (OCR): The individual characters are identified and converted into machine-readable text.
Post-processing: The recognized text is checked for accuracy and any errors are corrected.
There are several libraries and tools available for implementing text recognition with computer vision, such as OpenCV, Tesseract OCR, and Pytesseract. These tools can be integrated into applications or used as standalone programs to extract text from images or video.
Text recognition with computer vision has a wide range of applications, from digitizing paper documents to automatic license plate recognition for traffic enforcement. It can also be used to extract information from social media images or other unstructured data sources.
Nowadays, there are numerous applications. In particular:
- Verification of the content of the labels in the packaging
- Check the expiration dates on food packages
- Prevention of errors in books
- Reading the text engraved on metal
- Verify that the human readable text matches the 2D code
- Check the orientation of the printed text
Character recognition is also present in:
- Reading the Vehicle Identification Number (VIN) on numerous parts of the car
- Reading handwritten characters
- Identification of special characters
- Traceability of printed or handwritten text
- Reading and registration of car plates
- Data entry
Use OCR. Avoid headache in the text review
We know that character recognition applications are often used in industry. Frequent industrial character recognition applications are:
- Reading characters in the form of scraping or embossing on a metal surface
- Reading from surfaces such as plastic or glass
- Read the label or print on the product
- Checking various characters on moving products on a conveyor belt
- Verify information such as serial number, expiration data
- Verification of information such as chassis number, VIN, product code
Text recognition with computer vision has a variety of industrial applications, some of which are:
Quality control: Text recognition can be used in manufacturing to detect and verify the presence and accuracy of product labels, barcodes, and other printed information.
Inventory management: Text recognition can help automate the process of tracking and identifying items in a warehouse, making inventory management more efficient.
Document processing: In industries such as finance and insurance, text recognition can be used to automatically extract and digitize data from documents such as invoices, receipts, and forms.
Maintenance and inspection: Text recognition can be used to identify and track equipment and parts in industrial settings, as well as to read and analyze maintenance logs and inspection reports.
Hazard detection: Text recognition can help identify warning signs and labels in hazardous environments, alerting workers to potential risks.
Robotics: Text recognition can be used to enable robots to recognize and interact with text-based inputs, such as command labels or identification codes.