In many companies and organizations, the digitalization of documents and papers plays a decisive role in the optimization of processes. It is necessary to extract the relevant information from invoices, files, forms, certificates, e-mails, PDF documents or other almost any other documents in order to be able to process them further.

Diverse application scenarios for automatic text recognition

The manual data entry, which was inevitably common in the past due to the lack of other options, is extremely time-consuming. Employees have to read documents, enter content and enter data. A process that is not only time-consuming, but also often prone to errors. In most cases, the solution is called “Optical Character Recognition”, or OCR for short. Also known simply as “text recognition” in German, OCR systems are able to recognize text within graphics – for example, from a scan or a PDF. The application possibilities are extremely diverse, especially in the field of digital information management. They range all the way to very high-performance full-text search functions in archive systems, which can incorporate content from a wide variety of data sources throughout the company.

The limits of technology

Although OCR technology is now basically working well, it is still coming up against its limits. This can be the case, for example, if a document is partially damaged (the typical “coffee stain” in the office) or if the handwriting is difficult to decipher. Special fonts and other languages – such as Arabic or Asian characters – can also put automatic text recognition to the test.

Convolutional Neural Networks revolutionize OCR solutions

However, the next generation of OCR software is already in the starting blocks. One of the magic words here is CNN. Logically, this does not refer to the US news channel, but the abbreviation in this case stands for Convolutional Neural Network. Such artificial neural networks are architectures from the field of deep learning. They are used, for example, for object recognition in photographs, but are also increasingly being used in a new generation of solutions for automatic text recognition. Put very simply, CNNs are capable not only of recognizing letters, but of actually understanding text content. In the next step, this allows conclusions to be drawn about possibly missing or illegible information: For example, the software recognizes which word or phrase needs to be added logically. Apart from typical business applications in companies and organizations, this also opens up exciting application possibilities in science, for example. For example, OCR paired with Convolutional Neural Networks can be used in the IT-supported restoration of historical documents or books. This is because the intelligent software can also use fragments to predict what an author probably wrote several hundred years ago.

The right solution for you

Even though the data and documents you use are probably much younger: Next-generation OCR solutions are extremely exciting and have the potential for significant optimization in the course of digital transformation. Our experts will be happy to assist you in developing and implementing a concept tailored to your individual requirements.