Textract is a service that helps to extract handwriting, texts, and data from scanned documents. Most Textract services use Machine Learning technology to help the process. It reduces the manual labor that is still employed by various companies. Simple Optical Character Recognition (OCR) technology has also been in use for many decades. But it requires manual configuration and it must be updated every time the form changes. To reduce manual work and costs, Textract is being used increasingly by many organizations across the globe.
Using Textract, it gets comparatively easier to add document text detection and analysis to any application. It is helpful to:
- Detect any kind of typed or handwritten texts in various types of documents
- Extract information from structured data
- Process receipts and invoices
- Process identification documents issued by governments or private organizations
Textract mostly uses deep machine learning technology which learns every day from the new data. The interface is user-friendly and expertise of usage is not required. It uses simple and easy-to-use APIs to extract and read millions of images and documents daily.
What are the benefits of using Textract
- Integration of text document extraction into apps of choice – Text API can be used to build text detection in any web, mobile, or connected device application. Using deep-learning technologies, Textract helps to identify and extract handwritten and typed texts from a variety of documents, tables, excel sheets, invoices, forms, etc.
- Scalable document analysis availability – Textract makes it possible for an organization to detect and analyze millions of images and documents daily with minimum errors. This accelerates the decision-making process with easy data access capabilities.
- The benefit of cost reduction – With solution companies emerging there are effective subscription models where the charge is based on the number of documents analyzed and it significantly reduces the expense of the organization.
- Better security – There is an improvement of security and compliance through encryptions, data privacy, security controls, and support compliance standards such as GDPR, HIPAA, etc.
- Additional human review implementation – Human review can be easily implemented using Augmented AI. It manages nuanced and sensitive workflows and it can audit predictions. It helps to reduce errors and keep up with the change in the market.
Where can we see the implementation of Textract?
- Financial Services – Textract is used to extract critical business data including mortgage rates, applicant names, invoices across a variety of financial forms to process interest rates and mortgage rates in a matter of minutes.
- Healthcare sector – Patient data is extracted automatically from admission forms, insurance forms, authorization forms, etc. to serve the patient better and keep all the information in a streamlined manner in one particular platform. It eliminates manual review of work and reduces processing time.
- Public sector services – Textract extracts data from a variety of loan papers, mortgage papers, tax forms, and business applications with high accuracy of extracted data.
What are the limitations of Textract?
- Image formats can be JPG or PNG and documents must be in PDF format
- The size limit for image input is 10 MB
- Images are processed both synchronously whereas documents are processed only asynchronously
- Data is extracted in English, Spanish, German, French, Portuguese, and Italian, but it doesn’t specify the language which is extracted
- There is a limit on the transactions per second based on the region
How can KlearStack help with data extraction
With KlearStack’s solutions, the user can extract the data and store the extracted data in the form of excel. It thus provides flexibility to the user to manage the data in excel or keep it stored there. KlearStack used Intelligent OCR technology that is loaded with Artificial Intelligence for document processing.
KlearStack uses a combination of OCR, Natural Learning Processes (NLP), and Machine Learning (ML) to extract data from any form of unstructured data. With traditional OCR technology, it was a challenge to understand the meaning of the extracted data. But now with ML and NLP technology, the data can be given a meaning and thus can be utilized in a better way. The self-learning model increases the efficiency of the system through feedbacks and helps to reduce errors and gain accuracy.
To choose the best for the organization, it is advisable to go through the pros and cons of all technologies and to make the decision based on the company’s needs and future goals.