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In the business world, everything starts and ends with the document, whether it is a deal for cooperation, an NDA, documentation, or HR files. Artificial Intelligence (AI) rapidly changes how we manage and interact with documents. Thanks to modern AI technologies, it is easier now to collect, detect, and structure documents, reducing time and costs in the process. Since PDFs are recognized as globally accepted business document standards, AI facilitates the review and comparison of multiple docs in less time and with better results. To introduce you to more AI abilities in the document world, we’ve prepared this article.
Intelligent Document Processing, or IDP, uses AI algorithms for accurate document management. IDP may contain Optical character recognition (OCR), computer vision, machine learning (ML) and deep learning models, and natural language processing (NLP). According to research, the global intelligent document processing market is expected to reach $ 8,045.81 million by 2028. The business leverages significantly from such technical assistance, and the number of its potential features grows with customers’ demands. Among the popular characteristics of IDP are text recognition, smart tagging and annotating, and machine translation.
Text detection and recognition (OCR)
Optical character recognition (OCR) is sometimes used for text recognition due to its ability to detect and extract data from scanned documents or image-only PDFs into regular document format. Yet, OCR can do much more than that. Thanks to Neural Networks, the program detects hand-written text, its style, and languages. This process is very convenient while working with historical or legal documents, allowing users to change the format, edit text, and use search engines to find specific wording.
Optical character recognition in PDFs or handwritten text involves scanning or simply taking a photo of the document. After the image is loaded into the program, it performs word and character segmentation, which is detecting each individual word and then the character in it. After segmentation, each character including spaces, commas, etc needs to be classified, for this various feature extraction methods are used. Different feature extraction methods are designed for different representations of the characters, such as solid binary characters, character contours, skeletons (thinned characters), or gray-level sub-images of each individual character.
OCR has many benefits:
▪ Information becomes more readable
▪ Processing of OCR information is fast.
Allows converting paper documents into electronic ones
Automatic summarization and annotation
Business is facing dozens of documents constantly. It is a painstaking and time-consuming job. Yet, it needs to be done and it needs to be done correctly. With the use of AI architectures, this process can be accelerated and simplified. The main goal of text summarisation is to provide a comprehensive and relevant summary of the given text, covering its main points. To achieve this result, engineers use two primary approaches: extraction-based and abstraction-based summarisation.
▪ Extraction-based summarisation selects sentences or phrases without text modification. With this approach, you receive a summary of the document’s top N most essential sentences.
▪ Abstractive summarization uses Natural Language Generation (NLG) to rephrase and compress the original document. This approach is more complex and experimental than the extraction-based approach, however, with the current state-of-the-art NLP models this method gives much better results
While working with an enormous number of documents, it is wiser to tag them with corresponding labels. Or you can use smart tagging instead. The smart tag uses natural language processing, text analytics, and data-mining technologies to extract information from unstructured documents. Using this technology you can tag the most important things fast and accurately. In addition, with proper tagging, you can organize and customize your documents to search for information quickly and effortlessly.
The ability to understand and instantly translate one language into another is one of the Natural Language Processing (NPL) benefits. Google Translate is an excellent example of such integration, where NLP deep learning models provide the translation of sentences and words and are constantly improving. Modern machine translation models generate translations based on a deep understanding of words and the context where they are used, instead of relying on word-for-word translation. That’s why they can easily translate even proverbs, idioms, as well as jargon. Machine translation is handy when a document contains multiple examples of different languages. This technology saves priceless time and costs.
Sensitive information detection
Loss of sensitive information costs companies a fortune. In previous years the cost of a data breach reached $3.62 million. That is why detecting and properly collecting sensitive data may be life-saving. AI solutions can see data based on the text’s features (ID, phone number, email address, etc.). Once the words are detected, they are structured in a specific order. By using this technology, you can precisely select your data and secure it.
Benefits for business
▪ Data extraction option: Thanks to the OCR option, extracting information from scanned documents and photo files and editing them in minutes is easier. By training AI models, users can extract data from the document, automatically pulling insights from the text. Model understands its context, processes the document content more effectively, and takes action based on this information. Even more, models can be trained to recognize names, invoices, etc., and automatically forward them to users or employees.
▪ Automation of Data Processing: Manual data processing is lasting and vulnerable to human errors process, but with AI tools, it can be automated. Due to tech enhancement, it is easier to store, manage, and extract required information whenever it is needed. It saves time and costs and improves workflow.
▪ Business empowerment: Since AI can extract the required information from massive data stocks, companies can significantly leverage this fact. The power of data opens many business opportunities like risk management, higher agility, and valuable insights. AI revolutionized the document world and made its management smoother and operational time lesser.
▪ Optimized document management: Document management which includes data detection, tagging, and sorting, allows authorized users to receive information effectively. In addition, AI empowerment diminishes human checks.
▪ Better customer experience: Applying ML models to repetitive processes like email checking, feedback, and other client-customer communication helps businesses understand their sentiments and predict customer behavior. Such aid becomes very handy in sales, marketing, and customer support and enhances user experience.
▪ HR assistance: By adding AI tools to internal documents, it can evaluate and track employee productivity patterns. This information reveals great insights. Similarly, adding an AI tool to the recruitment process can sort applications and select the most fitting candidates for positions. This excludes manual interaction and requires human-to-human interaction only during the interview.
Intelligent documenting is taking over the modern business world, making it necessary for the proper time and cost optimization. You open a door with multiple opportunities by implying Artificial Intelligence to your product and even documentation. Anything from doc management to machine translation, OCR, and ending with your custom requirement.
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The incorporation of AI in documentation is not just a trend it is the empowerment that allows automating the processes, making them surefire and cost-optimizing. Read more about it in less than 5 mins.