Modern document-parsing technologies are generally smart enough to take in the gist of documents. They can read for precise meaning and understand the broad context. However, intelligent Document Parsing requires programs to take advantage of the options available in today’s software to read, import, manage and share documents. More intelligent document parsing means, for example, the ability to create merged copy/print versions instead of the practice of having numerous versions of a single document or paper output.
For instance, the IBM Document Conversion Workbench (DCW), Java Developer Kit (JDK) 6, includes the Document Parser API, which eases the task of creating software to read text or HTML files. This blog clarifies any questions on the smart way to document parsing and should provide a wonderful place to start if you want to learn more about it.
Source: Towards Data Science
What is Document Parsing?
Document parsing is the process of extracting information from an electronic document. It can be either structured or unstructured.
Papers in a variety of forms, including Word documents, PDF files, XML files, and HTML pages
Optical character recognition (OCR) is an electronic conversion of scanned printed material into editable and searchable text. The goal of OCR is to sort solid-print documents by extracting the textual content and turning it into a document that is easy to edit and search.
For instance, images converted to text using OCR include printed documents, handwritten documents, photographs, and hand-printed signs. The process extracts the characters of a particular font from an image to convert it into another form of representation, usually the computer’s default character set.
It’s a way of text computing that is concerned with making sense of documents comprehensively. It can be simplified to explain it in simpler terms. The system used the divided paper to extract the different algorithms implemented in the rules. This system can be complex or straightforward, depending on whether you plan to conduct an e-commerce website or a simple blogging site.
Uses of smart document parsing include business, education, and medicine. It helps to present information in a structured format to understand data better. According to Valuates, the global Image Recognition market is expected to grow by US$ 58920 million by 2026, up from US$ 20720 million in 2019.
How Does it Work?
Document parsing work starts with Data Extraction taking data out of existing documents or files. Data extraction usually involves optical character recognition (OCR). First, it recognizes text in images and pdf, HTML files, and other documents. Then, it converts it to machine-readable text and uses an index or database to find where it is within the larger composition.
Document parsing involves extracting documents like emails, text messages, phone calls, and other notes. In addition, document parsing is used for tasks like sentiment analysis (finding the subjective ‘feel’ of a document), finding specific information in a copy, and categorizing data according to a set of documents tagged as belonging to a particular category.
A parser is a program that reads text and extracts data. A corpus is the body of text used by the parser to learn to remove that data. Most people are familiar with search engines, the most common type of parser—a web crawler.
According to a new report by Reports and Data, the global natural language processing (NLP) in healthcare and life sciences is forecast to reach USD 4,799.6 Million by 2028.
Why do we need Document Parsing?
Document parsing is essential for almost all companies like banks, insurers, and airlines. Document parsing involves extracting information from the available documents like checks, contracts.
The database will store extracted data. We need Document Parsing to extract data from documents and create an index that we can later search. Most of today’s search and analysis tasks require us to retrieve and analyze content more effectively.
Smart Document Parsers
On top of dumb document parsers, brilliant document parsers are first. Achieve smartness by calling the outer parser to get additional information about parsing a particular document section. In turn, this creates an intelligent document parser powerful in the hands of a skilled user.
Visionify Document Parsing
Visionify’s API-based document parsing solutions analyses any document image (PDF, PNG, JPEG) and turn it into intelligent text. For example, traditional document parsing might provide you with the text read; Visionify’s APIs will give you headings, sub-sections, tables, figures. In addition, we have customized solutions for Label parsing, which will decode bar-codes, QR codes and provide an easy-to-use endpoint for integrations with your ERP: the platform scans, converts, indexes, and archives documents for storage retrieval. As a result, Visionify simplifies your document management process by ensuring maximum retention control, accuracy, and efficiency.
Text, tables, charts, and photos are extracted from any PDF document using APIs. In addition, it recognizes the page layout automatically and provides an easy-to-understand page tree with precise dimensions.
Intelligent Document Quality Processor
The Google Intelligent Document Quality Processor is an artificial intelligence platform developed by the Google Brain Institute. It is for detecting errors in the text. To achieve this, it uses a neural network to identify spelling errors, grammatical mistakes, and even inappropriate sources. It does this by scanning works for patterns associated with specific errors.
Google made the program to automate fact-checking, but it has many other potential applications, including stylistic analysis and text editing. This tool is for processing large numbers of documents in batches. This tool finds incorrect content within a specific document format at scale. Google Intelligent Document Quality processor achieves near 100% accuracy of document annotation.
For instance, The Document Quality Processor is the first step of Google’s new spam-fighting initiative. It’s about better understanding the text of an email, looking at each sentence for polarity, sentiment, syntax, key terms, and more. The goal involves training hundreds of thousands of spammed documents to build a classifier capable of identifying spam with reasonable accuracy.
UiPath Document Understanding
UiPath Document Understanding works by deep learning, natural language processing, and a set of computer vision and image processing algorithms. Together result in a better way to do OCR on paper, loose scrawled, or otherwise hard to read.
UiPath Document Understanding framework is a portion of UiPath Studio that allows you to parse and analyze text from scanned documents, business cards, customer records, electronic medical reports, and other resources. You can enlist the assistance of this framework to make it easier for your robot to read documents and understand their content.
Document Parsing with Programming Languages
PDF Parsing with Python
PDFs are everywhere. If you have a large body of PDFs and wish to extract information from them, either for your purpose or as a service to others, learning to parse through them using Python is a good idea. The ability to parse PDFs is essential if you’re running a business related to any publishing, whether it’s technical manuals or scientific research papers. To Cairo, programs like PDFMiner and pdf deal with this problem by introducing a Python-specific library to interface with the PDF.
Document Parsing recognizes what to do, presents the user with the necessary information, and assists him through the process. It should also ensure that all necessary studies have worked successfully. We may call this approach a Smart way to Document Parsing.
Visionify‘s motto is to make document imaging systems more efficient in every aspect. We’re constantly collaborating with our customers to develop new concepts and techniques. That will result in more efficient document imaging systems and higher yield across the entire life cycle of document imaging workflows. Contact us to get a live demo.