![]() However, semantic analysis allowed clearer extraction of coherent texts as well as a much richer set of information for further processing. The preservation of text order was made challenging by the presence of document noise, embedded images, a columnar format and other difficulties. This was a particular challenge for DGS in digitizing a corpus of art journals dating back to the nineteenth century. Without some level of semantic understanding, it can be difficult to determine textual content that is not clearly reproduced and consistently ordered. OCR technologies have been around for many years, but have always had their limitations. With these tools, more in-depth digital reading is possible than with basic OCR (optical character recognition). Entity linking – to contextualise the extracted entities with reference to online sources.Named entity recognition – to pick out references to people, places, dates and other named entities.Sentiment analysis – to identify the overall tone (e.g.Key phrase extraction – to isolate the main thematic points of the text.Language detection – to determine the text’s language (English, Italian, German, etc.).For this, you can make use of Azure’s vision APIs in combination with its NLP (natural language processing) services. Using these services, you can build computer vision, speech and textual analysis into your apps.įor document digitization, the first stage is to read text from images. The services are accessed through REST APIs and client library SDKs using popular languages like Python. They open up access to the most up-to-date AI techniques to developers without their needing to be experts in the field. They also include the Azure OpenAI service. Using Azure Cognitive ServicesĪzure Cognitive Services comprise a suite of tools that leverage four key areas of intelligent processing: vision, speech, language and decision-making. Using Python to leverage Azure AI services, you can take advantage of semantic analysis to extract, summarise and categorise documents. Modern digitization techniques go much further than simple OCR (optical character recognition). It is important, however, to choose those that can work efficiently and intelligently. When digitizing a physical library, you have many tools at your disposal. Step by Step: Digitizing a Physical Library with Python and Azure Python can be used in combination with Azure AI services for extracting, sumarising and categorising documents. However, modern data security standards can now provide excellent protection against malicious access attempts. Documents that are mislaid or inappropriately taken from secure locations risk revealing sensitive data. But it’s too easy to forget how vulnerable physical resources are to breaches of security. Securityīusinesses are rightly concerned about digital security. However, digital resources can be readily backed up to numerous destinations and intelligent validity checks can quickly flag up human errors. Handwritten notes are especially subject to mistakes as well as misreading. Papers can easily be mislaid and duplicates are not always available. Physical document processing is always risky. By combining comprehensive metadata with AI-powered search, digital resources can be found near-instantaneously, even with limited information. Even the most nimble administrator cannot compete with the speed and reach of digital search techniques. The volume of data in modern business means paper documents are just not practical anymore. Going paperless removes these harms entirely. Even recycled paper has significant environmental costs in the production process. Trees can be replanted of course, but the effects take time to normalise. Most paper is made from trees and deforestation is reckoned to account for around 10% of global warming. Perhaps the most obvious sustainability problem with paper is the resource use in its manufacture. Key Benefits to Adopting a Paperless Strategyįirst let’s look at some of the key advantages of going paperless. We’ll look at the benefits and run through some vital digitization best practices. Concerns over data availability and security can be allayed, digitization techniques are much more reliable and information management has reached a level of sophistication that leaves paper sorting in the dust.įor this guide, we’ve teamed up with cyber services experts DGS to learn more about their own digitization processes using Python and Azure. Using cloud infrastructures like Azure and AI technology, going paperless is now more realistic than ever. ![]() Now, with sustainability concerns increasingly a business priority, dreams of the office of the future may finally be realised. The paperless office has been predicted since at least the dawn of desktop computers in the 1970s.
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