OCR, or Optical Character Recognition, is a technology used to convert images of printed or handwritten text into editable, digital text. This technology allows computers to recognize and interpret the characters present in a scanned image or document, then convert them into text that can be manipulated, searched, edited and stored electronically.
OCR is often used to automate data entry from documents such as books, articles, invoices, forms, receipts and many more. It is also used in the digitization of historical documents, the conversion of physical documents into digital formats, and in various applications where it is necessary to process and analyze printed texts.
OCR works by analyzing the shapes and patterns of characters in an image, comparing them with a database of known character shapes. Sophisticated algorithms are used to detect and interpret the characters, even when the image quality is not optimal, for example in the event of distortion, noise or variations in size and font.
OCR plays an important role in transforming physical documents (sheets, books, microfiche, microfilm...) into digital formats, making it easier to manage, share and use them in a wide range of applications.
The languages supported by an OCR are determined by the character and pattern database used by the system. This database includes character shapes and patterns specific to each language. Languages that use similar or related characters may be easier to support together, while languages with more unique or complex scripts may require specific patterns. Our OCR technologies cover 210 languages.
At ADOC Solutions OCR technologies are widely used (since 1996) in many applications and fields. Here are some concrete examples of the use of optical character recognition (OCR) technology:
Document Scanning: OCR is used to convert physical documents such as books, articles, newspapers, and manuscripts into editable and digitally searchable text.
Archiving and document management: Companies use OCR to digitize and archive their physical documents, making it easier to find, retrieve and share information.
Information Extraction: OCR can automatically extract specific information from invoices, purchase orders, receipts, or forms, making it easier to manage finances and keep records.
Machine translation: Machine translation apps often use OCR to convert printed text in a foreign language into digital text, which can then be translated in real time.
Accessibility: OCR is used to make documents accessible to visually impaired or blind people by converting text to synthesized speech or Braille.
Business Card Recognition: Contact management apps use OCR to automatically extract contact information from business cards.
Text Search: Libraries and databases use OCR to make textual documents searchable online, allowing users to search for specific keywords in scanned documents.
Form Processing: Organizations use OCR to automatically extract data from completed forms, which speeds up the data processing and entry process.
Check processing: Banks use OCR to automate check processing by recognizing account numbers, amounts, and other important information.
License Plate Recognition: OCR is used in traffic surveillance systems to recognize license plate numbers on vehicles.
Archiving of historical documents: OCR can be used to digitize and transcribe old and historical documents, making it easier to access information for research and preservation.
Reading barcodes and QR codes: OCR can also be used to read and interpret information encoded in barcodes and QR codes found on products and labels.
These examples illustrate the versatility of the OCR technologies we use and its benefits in various fields to facilitate the transformation and management of textual data.
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