There is a wealth of solutions available, for example Tesseract OCR: but the challenge is managing the deployed service to ensure any solution scales horizontally without incurring too much time and effort, while fitting existing architecture. Read on to learn how Amazon Textract helps solve these challenges.
Amazon Textract is a machine learning service that automatically detects and extracts printed text, handwriting, and structured data (such as fields of interest and their values) from tables, images and scans of printed documents. It can successfully automate the digitization process, through a structured set of technology rules and commands.
Amazon Textract's machine learning models have been trained on millions of documents so that virtually any document type you upload is automatically recognized and processed for text extraction. When information is extracted from documents, the service returns a confidence score for each element it identifies so that you can make informed decisions about how you want to use the results. See more here.
We recently used AWS Textract to demonstrate how effectively it digitizes documents. Our task was to extract data from PDF files attached to incoming emails. To perform this manually took 5-10 minutes per PDF, since the operator had to physically check the extracted data. The number of documents requiring extraction grew monthly to the point where we were receiving hundreds each month and needed to automate the process.
When we began to use AWS Textract to digitize the process, we built a serverless pipeline to automatically process the emails, extract data from the attachments, refine it and store it in DynamoDB. Once the relevant data had been digitized, we could leverage the full power of AWS Services.
Our experience demonstrates that AWS Textract can perform efficient and automated digitization of existing documents, reducing the time and costs associated with performing manual tasks.
Esteban Uscanga-Olea, Cloud Solutions Architect, T-Systems International GmbH