What is OCR recognition?
In general, optical recognition systems are trained to extract text from images or scanned documents and convert it into machine-readable text. Such systems can also evaluate information that is not exclusively text or forms. In combination with NLP – Natural Language Processing – and Machine Learning (ML) algorithms, OCR software can interpret the individual word, always based on the context. This is relevant for the automation of data extraction, for example.
Full vs. zonal OCR
In optical character recognition, a distinction is made between full and zonal OCR: Full OCR reads the entire document and processes the complete text content. This achieves comprehensive data extraction. In contrast, zonal OCR (also known as zone OCR) enables greater specialization as it concentrates on specific areas in a document. The choice between full or zonal OCR depends on various factors such as the type of document, the information required and the intended use. A combination of both optical character recognition methods is also conceivable and useful.
Use and advantages of optical character recognition in companies
Most companies receive information in the form of printed media, such as forms, invoices and other documents in paper form. This large amount of paper not only takes up a lot of space for storage, but also poses a challenge for processing. This is where the use of OCR technologies comes in handy.
What are the advantages of OCR recognition?
OCR text recognition offers numerous advantages for both companies and individuals. Within document processing, OCR technologies offer two main benefits: they minimize manual data entry and increase efficiency.
In addition to these basic advantages, the use of OCR opens up further benefits:
- Searchable texts: OCR solutions make printed or handwritten texts searchable. This conversion enables a targeted and fast search for specific information.
- Improved data quality: The use of OCR technologies minimizes human error during manual data entry. This increases accuracy and reliability.
- Optimization of processes: OCR improves workflows by making text extraction automatic.
- Saving costs and human resources: As fewer human resources are required for manual data entry, these can be invested in more demanding tasks.
- Archiving and accessibility: OCR technologies guarantee efficient archiving of documents and make it much easier to access relevant information.
- Versatile applications: Whether in the healthcare industry, finance or other sectors, OCR technologies are extremely flexible and can be used for diverse document types.
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How it works: How OCR works in 4 steps
The process of optical character recognition can be illustrated in the following steps:
- First, the file or document is scanned and broken down into its individual elements (text, images, tables, etc.). This is followed by processing in which contrast and brightness are optimized.
- Shapes, patterns, numbers and symbols are analyzed. These features are compared with already known characters to enable assignment to the corresponding letters, numbers and symbols.
- The recognized characters are converted into machine-readable text and stored digitally. This step forms the core of OCR technology.
- Some OCR software offers additional functions and is able, for example, to create annotated PDF files from the extracted text data.
Text recognition forms the core of OCR technology and is based on two basic techniques: pattern matching and feature extraction. Pattern matching compares an isolated character representation (known as a glyph) with a similar stored glyph. This method works particularly well with scanned documents that are written in a known font. Feature extraction, on the other hand, breaks down the glyphs into individual features such as lines, loops and intersections and then uses them to determine the best match in the database of stored characters.
Areas of application for OCR
The areas of application for OCR are extremely diverse and range from office work to archiving. It is not just about increasing efficiency, but also about saving time.
One specific example is the automatic capture of data from paper documents using OCR in the field of document management. This process involves capturing data from paper documents, reducing manual data entry and automatically transferring invoices, forms and receipts to digital systems.
In the financial sector, OCR is used to process checks, invoices and other financial documents. Here, the technology ensures that financial data is captured accurately and quickly, making accounting and payment processing faster and more efficient.
In healthcare, OCR is used to digitize patient records, supporting the creation of electronic health records. This enables improved patient management, which has a positive impact on the quality of patient care.
OCR versus AI
The primary function of a pure OCR solution is to turn an image file (e.g. a scanned invoice) into machine-readable text. This requires special techniques, including AI (artificial intelligence), to recognize the different characters. Conventional OCR is mainly limited to recognizing individual characters and glyphs, without being able to interpret words or sentences.
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