How to implement Natural Language Processing (NLP)

Calendar Icon2023-01-09
Calendar Icon

How to implement Natural Language Processing (NLP)

An introduction to NLP and how Amazon Textract works

Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI) that enables machines to understand the human language. NLP combines computational linguistics (modeling of human language) with statistical, machine learning, and deep learning models. Together, these technologies enable computers to process human language in the form of text or voice data to ‘understand’ its full meaning, and understand the speaker or writer’s intent and sentiment.

NLP drives computer programs that translate text from one language to another, respond to spoken commands, and summarize large volumes of text rapidly. You may have already used NLP when interacting with your phone’s voice assistant but NLP also plays a bigger role in enterprise solutions that help increase employee productivity and simplify business processes.


Documents are important—for records, communication, collaboration, and even transactions. Yet millions of times per day, users rely on information that is locked in documents. This is a broad challenge across industries—affecting anyone who sends and receives paper documents, faxes, digital files, and/ or needs to keep records. It especially affects highly regulated industries including finance, healthcare, and government.

Organizations in all industries have a large number of physical documents and it can be difficult to extract text from a scanned document when it contains formats such as tables, forms, paragraphs, and check boxes. Extracting and analyzing text from images or PDFs is a classic machine learning (ML) and natural language processing (NLP) problem. When extracting the content from a document, you want to maintain the overall context and store the information in a readable and searchable format. Processing documents requires manual effort, traditional optical character recognition (OCR) software, and rules/template-based extraction. But this is expensive, error-prone, time-consuming.

In this article, we will discuss how AWS Textract works and how it can help us to implement NLP and facilitate the extraction of information from documents or images.

Now, let’s get started!

Amazon Textract is a document analysis service that detects and extracts printed text, handwriting, structured data (such as interest fields and their values), and tables from document images and scans. Amazon Textract’s machine learning models have been trained on millions of documents so that virtually any document type that is uploaded is automatically recognized and processed for text extraction. When information is extracted from documents, the service returns a confidence score for each item it identifies, so that you can make informed decisions about how to use the results.

For example, when extracting information from tax documents, custom rules can be set so that any data extracted with a confidence score of less than 95% is flagged. In addition, all extracted data is returned with the coordinates of the bounding box, which is a rectangular frame that completely encompasses each identified data, so that it is possible to quickly identify where a word or number appears in a document.

Amazon Textract operations can process document images that are stored on a local file system, or document images stored in an Amazon S3 bucket. You specify where the input document is located by using the Document input parameter. The document image can be in either PNG, JPEG, PDF, or TIFF format. Results for synchronous operations are returned immediately and are not stored for retrieval. However, Amazon Textract also provides an asynchronous API that you can use to process multi page documents in PDF or TIFF format. You can also use asynchronous operations to process single-page documents that are in JPEG, PNG, TIFF, or PDF format.

Amazon Textract can detect printed text and handwriting from the standard English alphabet and ASCII symbols, and can extract printed text, forms, and tables. It also extracts explicitly tagged data, implicit data, and line items from an itemized list of goods or services from almost any English invoice or receipt with no templates or configuration required, and finally it can get specific or implied data, such as names and addresses.

Some of the most common use cases for Amazon Textract include:

Creating smart search indexes—simplifying workstreams and tasks like searching for names or policy numbers in a sea of documents, building automated workflows for document processing—processing millions of document pages in hours and sending key pieces of information to the relevant system or team, extract text for natural language processing (NLP), extract text for document classification, analyze trends in data over time using additional ML techniques, performing RPA functions—to automate workflows and improve business agility, and many more, using some of the features and capabilities of AWS Textract (Optical Character Recognition, form extraction, table extraction, bounding boxes, adjustable confidence thresholds).

In general, Amazon Textract lets you include document text detection and analysis in your applications. With AT you can extract text from a variety of different document types using both synchronous and asynchronous document processing. Amazon Textract uses artificial intelligence to “read” documents as a person would, to extract not only text but also tables, forms, and other structured data without configuration, training, or custom code. The extracted text can then be saved to a file or database, or sent to another AWS service for further processing.

Processing paperwork en masse can take extraordinary amounts of time and effort, with discrete pieces of information needing to go to various systems or teams, but Amazon Textract provides you with the ability to automatically process forms and extract information from business documents, decreasing the level of human intervention necessary.

When your business requires it. For example, financial institutions and lenders can automate loan applications by using the information contained in documents to initiate all of the necessary background and credit checks to approve the loan— so that customers can get instant results of their application rather than having to wait several days for manual review and validation.

As you weigh your options for data extraction and text recognition from your documents we hope that you have found this AWS Textract review helpful. Amazon Textract enables applications to integrate so that the documents or images with textual data from various representations of text in the form of raw text, forms, tables are easily extractable. In general, with Natural Language Processing we can create applications that can extract text from structured and unstructured documents with tremendous accuracy. Although continuously evolving, NLP has already proven useful in multiple fields. The different implementations of NLP can help businesses and individuals save time, improve efficiency and increase customer satisfaction.


postViewer Strapi

The Future of Mobile Apps: How the Latest Technologies are Transforming the User Experience

postViewer Strapi

Why we love to build the best software development teams

postViewer Strapi

How to maximize Software Quality with Nearshore Quality Assurance

Meaningful Work, Global Impact.

© 2023 GoLabs. All rights reserved.


Costa Rica

get in touch