Document processing

Applications

Automatic document processing applies to a whole range of documents, whether structured or not. For instance, in the world of business and finance, technologies may be used to process paper-based invoices, forms, purchase orders, contracts, and currency bills.^[10] Financial institutions use intelligent document processing to process high volumes of forms such as regulatory forms or loan documents. ID uses AI to extract and classify data from documents, replacing manual data entry.^[11]

In medicine, document processing methods have been developed to facilitate patient follow-up and streamline administrative procedures, in particular by digitizing medical or laboratory analysis reports. The goal is also to standardize medical databases.^[12] Algorithms are also directly used to assist physicians in medical diagnosis, e.g. by analyzing magnetic resonance images,^[13][14] or microscopic images.^[15]

Document processing is also widely used in the humanities and digital humanities, in order to extract historical big data from archives or heritage collections. Specific approaches were developed for various sources, including textual documents, such as newspaper archives,^[16] but also images,^[17] or maps.^[18][19]

Technologies

If, from the 1980s onward, traditional computer vision algorithms were widely used to solve document processing problems,^[20][21] these have been gradually replaced by neural network technologies in the 2010s.^[22] However, traditional computer vision technologies are still used, sometimes in conjunction with neural networks, in some sectors.

Many technologies support the development of document processing, in particular optical character recognition (OCR), and handwritten text recognition (HTR), which allow the text to be transcribed automatically. Text segments as such are identified using instance or object detection algorithms, which can sometimes also be used to detect the structure of the document. The resolution of the latter problem sometimes also uses semantic segmentation algorithms.

These technologies often form the core of document processing. However, other algorithms may intervene before or after these processes. Indeed, document digitization technologies are also involved, whether in the form of classical or three-dimensional scanning.^[23] The digitization of 3D documents can in particular resort to derivatives of photogrammetry. Sometimes, specific 2D scanners must also be developed to adapt to the size of the documents or for reasons of scanning ergonomics.^[17] The document processing also depends on the digital encoding of the documents in a suitable file format. Furthermore, the processing of heterogeneous databases can rely on image classification technologies.

At the other end of the chain are various image completion, extrapolation or data cleanup algorithms. For textual documents, the interpretation can use natural language processing (NLP) technologies.