Editing Document processing

{{Short description|Digitalisation of analog documents}}
'''Document processing''' is a field of research and a set of [[production process]]es aimed at making an analog [[document]] digital. Document processing does not simply aim to photograph or [[Image scanning|scan]] a document to obtain a [[digital image]], but also to make it digitally intelligible. This includes extracting the structure of the document or the [[Document layout analysis|layout]] and then the content, which can take the form of text or images. The process can involve traditional [[computer vision]] algorithms, convolutional neural networks or manual labor. The problems addressed are related to [[semantic segmentation]], [[object detection]], [[optical character recognition|optical character recognition (OCR)]], [[Handwritten text recognition|handwritten text recognition (HTR)]] and, more broadly, [[Transcription (linguistics)|transcription]], whether [[Automation|automatic]] or not.<ref>{{Cite book
  |url=https://books.google.com/books?id=gYOpFlMXcs0C&q=%22document+processing%22+ocr&pg=PA368
  |title=Integrative Document & Content Management: Strategies for Exploiting Enterprise Knowledge
  |author1=Len Asprey |author2=Michael Middleton
  |date=2003  |publisher=Idea Group Inc (IGI)  |isbn=9781591400554}}</ref> The term can also include the phase of digitizing the document using a scanner and the phase of interpreting the document, for example using [[natural language processing]] (NLP) or [[image classification]] technologies. It is applied in many industrial and scientific fields for the optimization of administrative processes, mail processing and the digitization of analog [[Archiving|archives]] and historical documents.

==Background==
Document processing was initially as is still to some extent a kind of production line work dealing with the treatment of [[document]]s, such as letters and parcels, in an aim of sorting, extracting or massively extracting data. This work could be performed in-house or through [[business process outsourcing]].<ref>{{Cite book
    |url=https://books.google.com/books?id=g4dxNB05dgoC&q=document+processing+bpo&pg=PA47
    |title=Business Process Outsourcing: A Supply Chain of Expertises
    |author=Vinod V. Sople  |date=2009-05-25
    |publisher=PHI Learning Pvt. Ltd.  |isbn=978-8120338159}}</ref><ref>{{Cite book
|url=https://books.google.com/books?id=zdxbEwgfQzQC&q=%22document+processing%22+bpo&pg=PA167
|title=Outsourcing to India: The Offshore Advantage 
|author=Mark Kobayashi-Hillary
|date=2005-12-05 |publisher=Springer Science & Business Media |isbn=9783540247944}}</ref> Document processing can indeed involve some kind of externalized manual labor, such as [[Amazon Mechanical Turk|mechanical Turk]].

As an example of manual document processing, as relatively recent as 2007,<ref name="VisaDox">{{cite news |newspaper=[[The New York Times]]
   |url=https://www.nytimes.com/2007/12/02/us/02immig.html
   |title=Immigration Contractor Trims Wages
   |author=Julia Preston  |date=December 2, 2007}}</ref> document processing for "millions of visa and citizenship applications" was about use of "approximately 1,000 contract workers" working to "manage mail room and [[data entry clerk|data entry]]."

While document processing involved data entry via keyboard well before use of a [[computer mouse]] or a [[Image scanner|computer scanner]], a 1990 article in ''[[The New York Times]]'' regarding what it called the "[[paperless office]]" stated that "document processing begins with the scanner".<ref name="Paper.NYT">{{cite news|newspaper=[[The New York Times]]
   |url=https://www.nytimes.com/1990/07/07/business/paper-once-written-off-keeps-a-place-in-the-office.html
   |title=Paper, Once Written Off, Keeps a Place in the Office
   |author=Lawrence M. Fisher  |date=July 7, 1990}}</ref> In this context, a former [[Xerox]] vice-president, [[Paul Strassmann]], expressed a critical opinion, saying that computers add rather than reduce the volume of paper in an office.<ref name="Paper.NYT"/> It was said that the engineering and maintenance documents for an airplane weigh "more than the airplane itself"{{citation needed|date=April 2019}}.

==Automatic document processing==
As the ''[[state of the art]]'' advanced, document processing transitioned to handling "document components ... as database entities."<ref>{{cite magazine  |magazine=Object Magazine
   |date=February 1996  |page=51 |title=Unknown Title
   |author1=Al Young  |author2=Dayle Woolstein  |author3=Jay Johnson}}</ref>

A technology called automatic document processing or sometimes intelligent document processing (IDP) emerged as a specific form of [[Process Automation|Intelligent Process Automation]] (IPA), combining [[artificial intelligence]] such as [[Machine Learning]] (ML), [[Natural Language Processing]] (NLP) or [[Intelligent Character Recognition]] (ICE) to extract data from several types documents.<ref>{{Cite web|url=http://www.di.uniba.it/~ndm/pubs/esposito05icdar.pdf|title=Intelligent Document processing|date=2005-04-07|website=Department of Computer Science – University of Bari|access-date=2018-09-08}}</ref><ref>{{Cite book
    |url=https://www.computer.org/csdl/proceedings-article/icdar/2005/24201100/12OmNqIQS59
    |title="Intelligent Document Processing" in Proceedings. Eighth International Conference on Document Analysis and Recognition, Seoul, South Korea, 2005 pp. 1100-1104. doi: 10.1109/ICDAR.2005.144
    |author=[[Floriana Esposito]], Stefano Ferilli, Teresa M. A. Basile, Nicola Di Mauro |date=2005-04-01
    |publisher=  |doi=10.1109/ICDAR.2005.144
 |isbn=|s2cid=17302169
 }}</ref> Advancements in automatic document processing, also called Intelligent Document Processing, improve the ability to process [[unstructured data]] with fewer exceptions and greater speeds. <ref>{{Cite web |title=Intelligent Document Processing (IDP) |url=https://www.keymarkinc.com/intelligent-document-processing-idp/ |access-date=2024-07-12 |website=keymarkinc.com |language=en-US}}</ref> 

=== 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.<ref>{{cite patent |country=US|number=US7873576B2|status=active|title= Financial document processing system |pubdate=2011-01-18|gdate=2011-01-18|invent1=John E. Jones|invent2=William J. Jones|invent3=Frank M. Csultis|url=https://patents.google.com/patent/US7873576B2/en}}</ref> 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.<ref>{{Cite web|last=Bridgwater|first=Adrian|title=Appian Adds Google Cloud Intelligence To Low-Code Automation Mix|url=https://www.forbes.com/sites/adrianbridgwater/2020/03/09/appian-adds-google-cloud-intelligence-to-low-code-automation-mix/|access-date=2021-04-21|website=Forbes|language=en}}</ref>

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.<ref>{{cite journal |last1=Adamo|first1=Francesco|last2=Attivissimo|first2=Filippo|first3=Attilio|last3=Di Nisio|first4=Maurizio|last4=Spadavecchia|date=February 2015|title=An automatic document processing system for medical data extraction|url=https://www.sciencedirect.com/science/article/pii/S0263224114005016|journal=Measurement|volume=61|pages=88–99 |doi=10.1016/j.measurement.2014.10.032|bibcode=2015Meas...61...88A |access-date=31 January 2021}}</ref> Algorithms are also directly used to assist physicians in medical diagnosis, e.g. by analyzing [[Magnetic resonance imaging|magnetic resonance images]],<ref>{{cite journal |last1=Changwan|first1=Kim|last2=Seong-Il|first2=Lee|last3=Won Joon|first3=Cho|date=September 2020|title=Volumetric assessment of extrusion in medial meniscus posterior root tears through semi-automatic segmentation on 3-tesla magnetic resonance images|url=https://www.sciencedirect.com/science/article/abs/pii/S1877051720301994|journal=Orthopaedics & Traumatology: Surgery & Research|volume=101|issue=5|pages=963–968|doi=10.1016/j.rcot.2020.06.003|s2cid=225215597 |access-date=31 January 2021}}</ref><ref>{{cite journal |last1=Despotović|first1=Ivana|last2=Bart|first2=Goossens|last3=Wilfried|first3=Philips|date=1 March 2015|title=MRI Segmentation of the Human Brain: Challenges, Methods, and Applications|journal=Computational Intelligence Techniques in Medicine|volume=2015|pages=963–968|doi=10.1155/2015/450341|pmid=25945121|pmc=4402572|doi-access=free}}</ref> or [[Microscope|microscopic]] images.<ref>{{cite journal |last1=Putzua|first1=Lorenzo|last2=Caocci|first2=Giovanni|last3=Di Rubertoa|first3=Cecilia|title=Leucocyte classification for leukaemia detection using image processing techniques|journal=Artificial Intelligence in Medicine|date=November 2014|url=https://www.sciencedirect.com/science/article/pii/S0933365714001031|volume=63|issue=3|pages=179–191|doi=10.1016/j.artmed.2014.09.002|pmid=25241903|hdl=11584/94592|hdl-access=free}}</ref>

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,<ref>{{cite conference |url=https://www.zora.uzh.ch/id/eprint/191270/|title=Language Resources for Historical Newspapers: the Impresso Collection|last1=Ehrmann|first1=Maud|last2=Romanello|first2=Matteo|last3=Clematide|first3=Simon|last4=Ströbel|first4=Phillip|last5=Barman|first5=Raphaël|date=2020|book-title=Proceedings of the 12th Language Resources and Evaluation Conference|pages=958–968|location=Marseille, France}}</ref> but also images,<ref name="cini_archive_digitization">{{cite conference |url=https://www.ingentaconnect.com/content/ist/ac/2018/00002018/00000001/art00001|title=New Techniques for the Digitization of Art Historical Photographic Archives - the Case of the Cini Foundation in Venice|last1=Seguin|first1=Benoit|last2=Costiner|first2=Lisandra|last3=di Lenardo|first3=Isabella|last4=Kaplan|first4=Frédéric|date=April 1, 2018 |book-title=Archiving 2018 Final Program and Proceedings|publisher=Society for Imaging Science and Technology|pages=1–5|doi=10.2352/issn.2168-3204.2018.1.0.2}}</ref> or maps.<ref>{{cite conference |url=https://infoscience.epfl.ch/record/268282|title=A deep learning approach to Cadastral Computing|last1=Ares Oliveira|first1=Sofia|last3=Tourenc|first3=Bastien|last2=di Lenardo|first2=Isabella|last4=Kaplan|first4=Frédéric|date=11 July 2019|conference=Digital Humanities Conference|location=Utrecht, Netherlands}}</ref><ref>{{cite thesis|type=MSc|last=Petitpierre|first=Rémi|date=July 2020|title=Neural networks for semantic segmentation of historical city maps: Cross-cultural performance and the impact of figurative diversity|doi=10.13140/RG.2.2.10973.64484|arxiv=2101.12478
|url=https://www.researchgate.net/publication/343017681}}</ref>

===Technologies===

If, from the 1980s onward, traditional computer vision algorithms were widely used to solve document processing problems,<ref>{{cite journal |last1=Fujisawa|first1=H.|last2=Nakano|first2=Y.|last3=Kurino|first3=K.|date= July 1992
|title=Segmentation methods for character recognition: from segmentation to document structure analysis
|url= https://ieeexplore.ieee.org/document/156471|journal= Proceedings of the IEEE
|volume=80|issue=7|pages=1079–1092|doi= 10.1109/5.156471
|access-date=3 February 2021}}</ref><ref>
{{cite journal |last1=Tang|first1=Yuan Y.|last2=Lee|first2=Seong-Whan|last3=Suen|first3=Ching Y.|title=Automatic document processing: a survey
|url=https://www.sciencedirect.com/science/article/abs/pii/S0031320396000441|journal=Pattern Recognition|year=1996|volume=29|issue=12|pages=1931–1952|doi= 10.1016/S0031-3203(96)00044-1
|bibcode=1996PatRe..29.1931T |access-date=3 February 2021}}</ref> these have been gradually replaced by neural network technologies in the 2010s.<ref>{{cite conference |url=https://ieeexplore.ieee.org/document/8563218|title= dhSegment: A Generic Deep-Learning Approach for Document Segmentation|last1=Ares Oliveira|first1=Sofia|last2=Seguin|first2=Benoit|last3=Kaplan|first3=Frederic|date=5–8 August 2018 
|publisher=IEEE|location=Niagara Falls, NY, USA 
|conference=2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR)|doi=10.1109/ICFHR-2018.2018.00011
|arxiv=1804.10371}}</ref> 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.<ref>{{cite web |url=https://artmyn.com/|title= Revolutionary Scanning Technology for Art
|website=Artmyn|access-date=3 February 2021}}</ref> 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.<ref name="cini_archive_digitization"/> 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.

== See also ==
* [[Document automation]]
* [[Document modelling]]
* [[Data Processing]]
* [[Document Imaging]]
* [[Duplex scanning]]
* [[Text mining]]
* [[Workflow]]

==References==
{{Reflist}}

{{DEFAULTSORT:Document Processing}}
[[Category:Automatic identification and data capture]]
[[Category:Applied data mining]]
[[Category:Applications of computer vision]]