The experiment involved processing the entire corpus of approximately 18,500 documents in each OCR engine, measuring the accuracy against ground truth using the Information Science Research Institute (ISRI) tool, and comparing the results to two document collections of 322 English-language and 100 Arabic-language page scans.
· Project Initiation: - Define project objectives and scope. - Identify the types of documents to be processed (e.g., invoices, contracts, forms). - Establish a project timeline and milestones. · Setup AWS Infrastructure: - Create an AWS account and set up the necessary IAM roles and permissions. - Configure the AWS Textract service. · Data Collection and Preprocessing: - Gather a representative dataset of documents for testing and training. - Preprocess documents as needed (e.g., image enhancement, OCR correction). ...
The introduction of Optical Character Recognition (OCR) technology has brought about changes, in how businesses handle documents. This groundbreaking technology allows for the conversion of handwritten text into content that machines can easily read and understand. In this project we will explore the capabilities of Amazon Web Services Textract, an OCR service that combines cutting edge machine learning techniques with deep learning models. Our main focus is to delve into the architecture, features and practical applications of AWS Textract. We will thoroughly examine how this service excels at extracting text, forms and tables from types of documents like scanned papers, PDFs and images. By showcasing real world examples we aim to demonstrate how Textract seamlessly integrates with document management systems while enabling data analysis and automation processes. Furthermore, this article offers a detailed assessment of the efficiency and accuracy of three Optical Character Recognitio...
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