Document processing

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Document processing is a field of research and a set of production processes aimed at making an analog document digital. Document processing does not simply aim to photograph or 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 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 (OCR), handwritten text recognition (HTR) and, more broadly, transcription, whether automatic or not. [1] 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fields for the optimization of administrative processes, mail processing and the digitization of analog archives and historical documents.

Contents

Background

Document processing was initially as is still to some extent a kind of production line work dealing with the treatment of documents, 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. [2] [3] Document processing can indeed involve some kind of externalized manual labor, such as mechanical Turk.

As an example of manual document processing, as relatively recent as 2007, [4] 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."

While document processing involved data entry via keyboard well before use of a computer mouse or a 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". [5] In this context, a former Xerox vice-president, Paul Strassman, expressed a critical opinion, saying that computers add rather than reduce the volume of paper in an office. [5] It was said that the engineering and maintenance documents for an airplane weigh "more than the airplane itself"[ citation needed ].

Automatic document processing

As the state of the art advanced, document processing transitioned to handling "document components ... as database entities." [6]

A technology called automatic document processing or sometimes intelligent document processing (IDP) emerged as a specific form of 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. [7] [8] Advancements in automatic document processing, also called Intelligent Document Processing, improve the ability to process unstructured data with fewer exceptions and greater speeds. [9]

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.

See also

Related Research Articles

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Natural language processing (NLP) is an interdisciplinary subfield of computer science and artificial intelligence. It is primarily concerned with providing computers with the ability to process data encoded in natural language and is thus closely related to information retrieval, knowledge representation and computational linguistics, a subfield of linguistics. Typically data is collected in text corpora, using either rule-based, statistical or neural-based approaches in machine learning and deep learning.

<span class="mw-page-title-main">Optical character recognition</span> Computer recognition of visual text

Optical character recognition or optical character reader (OCR) is the electronic or mechanical conversion of images of typed, handwritten or printed text into machine-encoded text, whether from a scanned document, a photo of a document, a scene photo or from subtitle text superimposed on an image.

<span class="mw-page-title-main">Point cloud</span> Set of data points in three-dimensional space

A point cloud is a discrete set of data points in space. The points may represent a 3D shape or object. Each point position has its set of Cartesian coordinates. Points may contain data other than position such as RGB colors, normals, timestamps and others. Point clouds are generally produced by 3D scanners or by photogrammetry software, which measure many points on the external surfaces of objects around them. As the output of 3D scanning processes, point clouds are used for many purposes, including to create 3D computer-aided design (CAD) or geographic information systems (GIS) models for manufactured parts, for metrology and quality inspection, and for a multitude of visualizing, animating, rendering, and mass customization applications.

<span class="mw-page-title-main">Handwriting recognition</span> Ability of a computer to receive and interpret intelligible handwritten input

Handwriting recognition (HWR), also known as handwritten text recognition (HTR), is the ability of a computer to receive and interpret intelligible handwritten input from sources such as paper documents, photographs, touch-screens and other devices. The image of the written text may be sensed "off line" from a piece of paper by optical scanning or intelligent word recognition. Alternatively, the movements of the pen tip may be sensed "on line", for example by a pen-based computer screen surface, a generally easier task as there are more clues available. A handwriting recognition system handles formatting, performs correct segmentation into characters, and finds the most possible words.

<span class="mw-page-title-main">Image segmentation</span> Partitioning a digital image into segments

In digital image processing and computer vision, image segmentation is the process of partitioning a digital image into multiple image segments, also known as image regions or image objects. The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Image segmentation is typically used to locate objects and boundaries in images. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain characteristics.

Automatic summarization is the process of shortening a set of data computationally, to create a subset that represents the most important or relevant information within the original content. Artificial intelligence algorithms are commonly developed and employed to achieve this, specialized for different types of data.

<span class="mw-page-title-main">3D scanning</span> Scanning of an object or environment to collect data on its shape

3D scanning is the process of analyzing a real-world object or environment to collect three dimensional data of its shape and possibly its appearance. The collected data can then be used to construct digital 3D models.

Optical music recognition (OMR) is a field of research that investigates how to computationally read musical notation in documents. The goal of OMR is to teach the computer to read and interpret sheet music and produce a machine-readable version of the written music score. Once captured digitally, the music can be saved in commonly used file formats, e.g. MIDI and MusicXML . In the past it has, misleadingly, also been called "music optical character recognition". Due to significant differences, this term should no longer be used.

<span class="mw-page-title-main">Thomas Huang</span> Chinese-American engineer and computer scientist (1936–2020)

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Intelligent character recognition (ICR) is used to extract handwritten text from images. It is a more sophisticated type of OCR technology that recognizes different handwriting styles and fonts to intelligently interpret data on forms and physical documents.

<span class="mw-page-title-main">Book scanning</span> Process of converting physical media into digital media

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<span class="mw-page-title-main">Digital pathology</span>

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<span class="mw-page-title-main">Optical braille recognition</span> Automated recognition of braille characters

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<span class="mw-page-title-main">Scene text</span> Text captured as part of outdoor surroundings in a photograph

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<span class="mw-page-title-main">GigaMesh Software Framework</span> Software framework for processing and analyzing 3D mesh data

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<span class="mw-page-title-main">Studierfenster</span>

Studierfenster or StudierFenster (SF) is a free, non-commercial open science client/server-based medical imaging processing online framework. It offers capabilities, like viewing medical data (computed tomography (CT), magnetic resonance imaging (MRI), etc.) in two- and three-dimensional space directly in the standard web browsers, like Google Chrome, Mozilla Firefox, Safari, and Microsoft Edge. Other functionalities are the calculation of medical metrics (dice score and Hausdorff distance), manual slice-by-slice outlining of structures in medical images (segmentation), manual placing of (anatomical) landmarks in medical image data, viewing medical data in virtual reality, a facial reconstruction and registration of medical data for augmented reality, one click showcases for COVID-19 and veterinary scans, and a Radiomics module.

References

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  2. ↑ Vinod V. Sople (2009-05-25). Business Process Outsourcing: A Supply Chain of Expertises. PHI Learning Pvt. Ltd. ISBN   978-8120338159.
  3. ↑ Mark Kobayashi-Hillary (2005-12-05). Outsourcing to India: The Offshore Advantage. Springer Science & Business Media. ISBN   9783540247944.
  4. ↑ Julia Preston (December 2, 2007). "Immigration Contractor Trims Wages". The New York Times .
  5. 1 2 Lawrence M. Fisher (July 7, 1990). "Paper, Once Written Off, Keeps a Place in the Office". The New York Times .
  6. ↑ Al Young; Dayle Woolstein; Jay Johnson (February 1996). "Unknown Title". Object Magazine. p. 51.
  7. ↑ "Intelligent Document processing" (PDF). Department of Computer Science – University of Bari. 2005-04-07. Retrieved 2018-09-08.
  8. ↑ Floriana Esposito, Stefano Ferilli, Teresa M. A. Basile, Nicola Di Mauro (2005-04-01). "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. doi:10.1109/ICDAR.2005.144. S2CID   17302169.{{cite book}}: CS1 maint: multiple names: authors list (link)
  9. ↑ "Intelligent Document Processing (IDP)". keymarkinc.com. Retrieved 2024-07-12.
  10. ↑ USactive US7873576B2,John E. Jones; William J. Jones& Frank M. Csultis,"Financial document processing system",published 2011-01-18,issued 2011-01-18
  11. ↑ Bridgwater, Adrian. "Appian Adds Google Cloud Intelligence To Low-Code Automation Mix". Forbes. Retrieved 2021-04-21.
  12. ↑ Adamo, Francesco; Attivissimo, Filippo; Di Nisio, Attilio; Spadavecchia, Maurizio (February 2015). "An automatic document processing system for medical data extraction". Measurement. 61: 88–99. Bibcode:2015Meas...61...88A. doi:10.1016/j.measurement.2014.10.032 . Retrieved 31 January 2021.
  13. ↑ Changwan, Kim; Seong-Il, Lee; Won Joon, Cho (September 2020). "Volumetric assessment of extrusion in medial meniscus posterior root tears through semi-automatic segmentation on 3-tesla magnetic resonance images". Orthopaedics & Traumatology: Surgery & Research. 101 (5): 963–968. doi:10.1016/j.rcot.2020.06.003. S2CID   225215597 . Retrieved 31 January 2021.
  14. ↑ Despotović, Ivana; Bart, Goossens; Wilfried, Philips (1 March 2015). "MRI Segmentation of the Human Brain: Challenges, Methods, and Applications". Computational Intelligence Techniques in Medicine. 2015: 963–968. doi: 10.1155/2015/450341 . PMC   4402572 . PMID   25945121.
  15. ↑ Putzua, Lorenzo; Caocci, Giovanni; Di Rubertoa, Cecilia (November 2014). "Leucocyte classification for leukaemia detection using image processing techniques". Artificial Intelligence in Medicine. 63 (3): 179–191. doi:10.1016/j.artmed.2014.09.002. hdl: 11584/94592 . PMID   25241903.
  16. ↑ Ehrmann, Maud; Romanello, Matteo; Clematide, Simon; Ströbel, Phillip; Barman, Raphaël (2020). "Language Resources for Historical Newspapers: the Impresso Collection". Proceedings of the 12th Language Resources and Evaluation Conference. Marseille, France. pp. 958–968.
  17. 1 2 Seguin, Benoit; Costiner, Lisandra; di Lenardo, Isabella; Kaplan, Frédéric (April 1, 2018). "New Techniques for the Digitization of Art Historical Photographic Archives - the Case of the Cini Foundation in Venice". Archiving 2018 Final Program and Proceedings. Society for Imaging Science and Technology. pp. 1–5. doi:10.2352/issn.2168-3204.2018.1.0.2.
  18. ↑ Ares Oliveira, Sofia; di Lenardo, Isabella; Tourenc, Bastien; Kaplan, Frédéric (11 July 2019). A deep learning approach to Cadastral Computing. Digital Humanities Conference. Utrecht, Netherlands.
  19. ↑ Petitpierre, Rémi (July 2020). Neural networks for semantic segmentation of historical city maps: Cross-cultural performance and the impact of figurative diversity (MSc). arXiv: 2101.12478 . doi:10.13140/RG.2.2.10973.64484.
  20. ↑ Fujisawa, H.; Nakano, Y.; Kurino, K. (July 1992). "Segmentation methods for character recognition: from segmentation to document structure analysis". Proceedings of the IEEE. 80 (7): 1079–1092. doi:10.1109/5.156471 . Retrieved 3 February 2021.
  21. ↑ Tang, Yuan Y.; Lee, Seong-Whan; Suen, Ching Y. (1996). "Automatic document processing: a survey". Pattern Recognition. 29 (12): 1931–1952. Bibcode:1996PatRe..29.1931T. doi:10.1016/S0031-3203(96)00044-1 . Retrieved 3 February 2021.
  22. ↑ Ares Oliveira, Sofia; Seguin, Benoit; Kaplan, Frederic (5–8 August 2018). dhSegment: A Generic Deep-Learning Approach for Document Segmentation. 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR). Niagara Falls, NY, USA: IEEE. arXiv: 1804.10371 . doi:10.1109/ICFHR-2018.2018.00011.
  23. ↑ "Revolutionary Scanning Technology for Art". Artmyn. Retrieved 3 February 2021.
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