#Amazon Textract

[xaltius]

Top 10 Best OCR Models You Need to Know in 2025  

In an increasingly digital world, a surprising amount of critical information remains locked away in physical documents, scanned images, or unstructured digital formats. This is where Optical Character Recognition (OCR) technology steps in – converting various types of documents, such as scanned paper documents, PDFs, or images, into editable and searchable data.

But OCR is no longer just about basic text extraction. In 2025, advanced OCR models, powered by sophisticated AI and deep learning, are moving beyond mere character recognition to truly understand document layouts, extract structured data from complex forms, and even decipher diverse handwriting. The right OCR model can be the linchpin for digital transformation, automating workflows, enhancing data accessibility, and unlocking invaluable insights.

Here are the top 10 OCR models and technologies that are making waves and defining the landscape in 2025:

Cloud-Powered & Enterprise Solutions

These offerings typically provide robust, scalable, and often AI-infused solutions with extensive language support and pre-built models for common document types.

Google Cloud Document AI

Strength: More than just OCR, it's a comprehensive document processing platform. It uses specialized parsers trained on specific document types (invoices, receipts, contracts, IDs) to extract structured data, not just raw text. Its underlying OCR is highly accurate, especially for complex layouts and tables.

Why for 2025: Integrated with Google Cloud's broader AI ecosystem, it's ideal for businesses needing deep document understanding and automation across various industries, pushing beyond simple text extraction.

Amazon Textract

Strength: A machine learning service that goes beyond simple OCR to automatically extract text, handwriting, and data from scanned documents. It excels at identifying forms, tables, and key-value pairs, making it powerful for automating data entry from semi-structured documents.

Why for 2025: Part of the AWS ecosystem, Textract is known for its high accuracy and seamless integration into cloud-native applications, perfect for scalable document processing pipelines.

Azure Document Intelligence (formerly Form Recognizer)

Strength: Microsoft's offering provides powerful OCR alongside intelligent document processing. It supports pre-built models for common document types (invoices, receipts, business cards), custom model training for unique layouts, and layout analysis to preserve document structure.

Why for 2025: Its tight integration with Azure services and strong capabilities in understanding both printed and handwritten text, even with complex layouts, make it a top choice for enterprises leveraging Microsoft's cloud.

ABBYY FineReader PDF (and ABBYY Vantage)

Strength: A long-standing leader in OCR, ABBYY offers highly accurate text recognition across a vast number of languages (over 190). FineReader is excellent for converting scanned documents and PDFs into editable formats. ABBYY Vantage extends this to Intelligent Document Processing (IDP) with AI-powered data capture from complex business documents.

Why for 2025: Known for its precision and comprehensive language support, ABBYY remains a go-to for high-fidelity document conversion and advanced IDP needs, especially where accuracy in diverse languages is paramount.

Advanced Open-Source Models & Frameworks

For developers and researchers who need customization, specific integrations, or budget-friendly solutions.

Tesseract OCR (maintained by Google)

Strength: The most widely used open-source OCR engine. While traditionally needing pre-processing, its latest versions (Tesseract 4+ with LSTM-based engine) offer significantly improved accuracy, especially for line-level recognition, and support over 100 languages.

Why for 2025: It's a foundational tool, highly customizable, and perfect for projects where you need a free, powerful OCR engine with extensive language support. Often used as a baseline or integrated into larger systems.

PaddleOCR (Baidu)

Strength: A comprehensive, open-source toolkit for OCR that boasts strong performance across various scenarios, including complex layouts, multi-language support (over 80 languages, including complex Chinese/Japanese characters), and impressive accuracy, often outperforming Tesseract out-of-the-box on certain benchmarks.

Why for 2025: Its ease of use, robust pre-trained models, and strong community support make it an excellent choice for developers looking for a high-performance, flexible open-source solution.

docTR (Mindee)

Strength: An open-source, end-to-end OCR library built on deep learning frameworks (TensorFlow 2 & PyTorch). It focuses on document understanding, offering strong performance in text detection and recognition, particularly for structured documents and various fonts.

Why for 2025: Offers a modern, deep-learning based approach, known for good accuracy on challenging document types like scanned forms and screenshots. It's a strong option for developers building custom document processing workflows.

Emerging & Specialized Models

These models represent newer advancements, often leveraging vision-language models or focusing on specific niches.

Florence-2 (Microsoft)

Strength: A powerful vision-language model that excels at various computer vision tasks, including detailed OCR. Its ability to understand the spatial relationships between text and other visual elements makes it excellent for complex document layouts, scene text, and even visual question answering.

Why for 2025: As a versatile foundation model, Florence-2 pushes the boundaries of multimodal understanding, suggesting a future where OCR is deeply integrated with broader visual intelligence.

Surya

Strength: A Python-based OCR toolkit specifically designed for line-level text detection and recognition across 90+ languages. It's gaining popularity for its efficiency and accuracy, often touted as outperforming Tesseract in speed and recognition for certain tasks.

Why for 2025: For developers who need fast, accurate line-level OCR, especially in a Python environment, Surya offers a compelling lightweight alternative to larger models.

Mistral OCR (Mistral AI)

Strength: Launched recently in 2025, Mistral OCR is quickly gaining recognition for its robust performance on complex documents including PDFs, scanned images, tables, and even equations. It accurately extracts text and visuals, making it useful for Retrieval Augmented Generation (RAG) applications.

Why for 2025: As a product from a leading AI firm, it represents the cutting edge in highly accurate, context-aware OCR, especially for integrating document intelligence with advanced AI systems.

Key Trends Shaping OCR in 2025

Beyond Text: The focus is shifting from mere text extraction to comprehensive Document Understanding, including layout analysis, table extraction, and key-value pair identification.

AI Integration: OCR is increasingly powered by sophisticated deep learning models and integrated with larger AI pipelines, including Large Language Models (LLMs) for semantic understanding and post-processing.

Handwriting Recognition (ICR): Significant advancements are being made in accurately recognizing diverse handwriting styles.

Cloud-Native & API-Driven: Most leading solutions are offered as scalable cloud services with robust APIs for seamless integration into enterprise applications.

Multimodal OCR: Models are leveraging both visual and textual cues to improve accuracy and contextual understanding.

Choosing Your OCR Model

The "best" OCR model depends entirely on your specific needs:

For high-volume, structured documents (invoices, receipts): Consider cloud services like Google Document AI, Amazon Textract, or Azure Document Intelligence.

For broad language support and customizability (open-source): Tesseract or PaddleOCR are strong contenders.

For complex layouts and modern deep learning approaches: docTR, Florence-2, or Mistral OCR are excellent choices.

For specific tasks like line-level text detection: Surya offers a specialized solution.

The landscape of OCR in 2025 is dynamic and exciting. By understanding these top models and the underlying trends, you can choose the right tools to unlock the vast potential hidden within your documents and drive meaningful automation and insights.

[ai-news]

E.ON’s story highlights how a creative application of Amazon Textract, combined with custom image analysis and pulse counting, can solve a real-world challenge at scale. By diagnosing smart meter errors through brief smartphone videos, E.ON aims to #AI #ML #Automation

[djcomputing01]

AI Document Processing with Amazon Textract Boost Revenue & Saves

SEO Description

Artificial intelligence tools for manual document processing? Explore SNQS's success story with AI-powered document processing using Amazon Textract.

Struggling with manual PO processing? SNQS, a leading European-Middle Eastern merchandise management company, faced similar challenges. They turned to AI and achieved remarkable results.

Key Benefits:

Dramatically Reduced Errors: Lowered error rates from 30% to under 5%. Streamlined Operations: Decreased processing time by 70% and increased order fulfillment by 15%. Enhanced Efficiency: Increased order volume by 25% without additional staff. Boosted Revenue: Generated an extra €6 million in annual revenue. Cost Savings: Reduced manual processing costs by 60% and administrative overhead by 50%. Discover how AI can transform your PO management. Request a demo today!

SEO Keywords AI document processing, Amazon Textract, purchase order automation, order fulfillment increase, cost savings, revenue growth, SNQS case study document processing problems, Amazon Textract, AI document solution, error reduction, processing time optimization, real-time document tracking, Amazon Textract, merchandise management, order volume increase, operational efficiency, supplier relationship improvement,

Website link - https://www.djcomputing.io/snqs-case-study

[markwatsonsbooks]

AWS Ultimate Guide: From Beginners to Advanced by SK Singh

This is a very comprehensive book on AWS, from beginners to advanced. The book has extensive diagrams to help understand topics much easier way.

To make understanding the subject a smoother experience, the book is divided into the following sections:

Cloud Computing

AWS Fundamentals (What is AWS, AWS Account, AWS Free Tier, AWS Cost & Billing Management, AWS Global Cloud Infrastructure (part I)), IAM, EC2)

AWS Advanced (EC2 Advanced, ELB, Advanced S3, Route 53, AWS Global Cloud Infrastructure (part II), Advanced Storage on AWS, AWS Monitoring, Audit, and Performance),

AWS RDS and Databases (AWS RDS and Cache, AWS Databases)

Serverless (Serverless Computing, AWS Integration, and Messaging)

Container & CI/CD (Container, AWS CI/CD services)

Data & Analytics (Data & Analytics)

Machine Learning (AWS ML/AI Services)

Security (AWS Security & Encryption, AWS Shared Responsibility Model, How to get Support on AWS, Advanced Identity)

Networking (AWS Networking)

Disaster Management (Backup, Recovery & Migrations)

Solutions Architecture (Cloud Architecture Key Design Principles, AWS Well-Architected Framework, Classic Solutions Architecture)

Includes AWS services/features such as IAM, S3, EC2, EC2 purchasing options, EC2 placement groups, Load Balancers, Auto Scaling, S3 Glacier, S3 Storage classes, Route 53 Routing policies, CloudFront, Global Accelerator, EFS, EBS, Instance Store, AWS Snow Family, AWS Storage Gateway, AWS Transfer Family, Amazon CloudWatch, EventBridge, CloudWatch Insights, AWS CloudTrail, AWS Config, Amazon RDS, Amazon Aurora, Amazon ElatiCache, Amazon DocumentDB, Amazon Keyspaces, Amazon Quantum Ledger Database, Amazon Timestream, Amazon Managed Blockchain, AWS Lambda, Amazon DynamoDB, Amazon API Gateway, SQS, SNS, SES, Amazon Kinesis, Amazon Kinesis Firehose, Amazon Kinesis Data Analytics, Amazon Kinesis Data Streams, Amazon Kinesis ECS, Amazon Kinesis ECR, Amazon EKS, AWS CloudFormation, AWS Elastic Beanstalk, AWS CodeBuild, AWS OpsWorks, AWS CodeGuru, AWS CodeCommit, Amazon Athena, Amazon Redshift, Amazon EMR, Amazon QuickSight, AWS Glue, AWS Lake Formation, Amazon MSK, Amazon Rekognition, Amazon Transcribe, Amazon Polly, Amazon Translate, Amazon Lex, Amazon Connect, Amazon Comprehend, Amazon Comprehend Medical, Amazon SageMaker, Amazon Forecast, Amazon Kendra, Amazon Personalize, Amazon Textract, Amazon Fraud Detector, Amazon Sumerian, AWS WAF, AWS Shield Standard, AWS Shield Advanced, AWS Firewall Manager, AWS GuardDuty, Amazon Inspector, Amazon Macie, Amazon Detective, SSM Session Manager, AWS Systems Manager, S3 Replication & Encryption, AWS Organization, AWS Control Tower, AWS SSO, Amazon Cognito, AWS VPC, NAT Gateway, VPC Endpoints, VPC Peering, AWS Transit Gateway, AWS Site-to-Site VPC, Database Management Service (DMS), and many others.

Order YOUR Copy NOW: https://amzn.to/4bfoHQy via @amazon

[aws-solutions]

Celebal Technologies: Pioneering AWS Cloud Solutions for Business Transformation 

Celebal Technologies is a leading AWS Advanced Tier Services Partner, recognized for our deep understanding of core AWS services. This expertise empowers us to design, build, and manage robust cloud architectures that perfectly align with your unique business requirements. 

Our team boasts a comprehensive portfolio of advanced AWS certifications, solidifying our proven track record of delivering secure, scalable, and cost-effective solutions on the AWS platform. As one of the leading AWS consulting services, we leverage the full potential of AWS to craft and implement cloud solutions that unlock the power of your data and propel organizational transformation.  Our all-encompassing suite of services equips you to achieve your business objectives through cutting-edge technology. 

Unleashing the Power of AWS Solutions 

Celebal Technologies offers a comprehensive range of AWS cloud solutions designed to optimize your operations and unlock new possibilities on AWS: 

SAP BW Migration: Simplify migrating your SAP BW to cloud platforms like Redshift, Datasphere, or Databricks using our SAP BW Migration Acceleration Packages. These packages can significantly speed up the process by 50-60% thanks to built-in solutions that translate and analyze even the most complex SAP structures, logic, and data models. This ensures a smooth and efficient migration. 

Contact Center Intelligence: This AI-powered solution leverages AWS Bedrock and other Amazon Web Services to unlock valuable insights from calls. It analyzes past interactions, assists agents in real-time, and automates tasks. The result? A significant boost in efficiency – calls are 30% shorter, resolutions rise by 25%, escalations drop by 40%, and complex issues are handled 50% faster. Additionally, agents save a massive 90% of search time and automate forms with live transcriptions, tripling their overall productivity. 

CT-Miner: Unveiling hidden knowledge from massive documents becomes effortless with this automated system. Powered by deep learning, it securely processes large files and delivers real-time insights.  Search through documents, extract key details, ask questions in natural language, uncover hidden themes, generate summaries, handle multiple languages, automatically import data, and even perform semantic image searches – all within a secure AWS environment. 

SAS Migration: Unleash the full potential of your data with Celebal's seamless SAS workload migration to the Lakehouse Platform. This powerful AWS-based solution tackles scalability, performance, and workload variety head-on, ensuring a quicker and more cost-effective migration. By leveraging the Lakehouse Platform, you'll unlock a world of comprehensive analytics possibilities, empowering your organization with faster data processing and deeper insights. 

SAP PO to Invoice Reconciliation Solution: Streamline your purchase-to-pay process with this automated solution. It utilizes Cloud Platform Integration (CPI) to effortlessly extract emails, securely stores purchase orders (POs) in Amazon S3, and leverages Textract for intelligent data extraction.  By validating POs against Goods Received Notices (GRNs) and automating invoice posting within SAP, the system ensures data accuracy and facilitates open item processing.  This translates to error-free data capture, optimized resource allocation, and automated validation checks –  all contributing to seamless regulatory compliance. 

Procurement AI Assistant: Powered by the cutting-edge capabilities of Amazon Bedrock and Anthropic Claude, our GenAI solution revolutionizes procurement by seamlessly integrating SAP data with unstructured sources. This AI powerhouse automates tasks like requisitioning, supplier analysis, spend optimization, and inventory management, while providing valuable market insights. Experience significant productivity gains through features like automated inventory monitoring, purchase orders, streamlined sourcing processes, improved supplier communication, and real-time KPI tracking. 

Sales AI Assistant: Take your sales team to the next level with our AI solution. It seamlessly combines data from your SAP system and other sources to prioritize leads, automate reports, analyze customer sentiment, and track performance across all sales activities. This not only ensures clean CRM data but also empowers your team with automated analytics and reporting, driving excellence in cross-selling, upselling, and uncovering new opportunities. 

STARGen AI: Forget complex queries! STARGen AI is your key to unlocking insights from massive datasets. This revolutionary AI solution uses natural language processing to understand your questions and translate them into SQL queries for structured data tables. Powered by cutting-edge large language models like Anthropic Claude and Llama 2, STARGen AI makes data interaction effortless, allowing you to extract valuable information with ease. 

Partner with Celebal Technologies for a Successful Cloud Journey 

By partnering with Celebal Technologies, you gain access to a team of AWS experts who are passionate about helping you achieve your business goals.  We will work closely with you to understand your specific needs and challenges, and then design and implement a customized cloud solution that delivers optimal results. 

Contact Celebal Technologies today at enterprisesales@celebaltech.com and unlock the full potential of AWS services for your business! 

[kariniai]

Amazon Bedrock and the Rise of Compound AI Systems

Introduction

Generative AI has become a shared C-Level priority with many enterprises setting goals in their annual statement and numerous press releases. As Generative AI is gaining traction, there is much anticipation around their evolving model performance capabilities. However, as developers increasingly move beyond Generative AI pilots, the trend is shifting to compound systems. The SOTA results often come from compound systems incorporating multiple components rather than relying solely on standalone models. A recent study by MIT Research has observed that 60% of LLM deployments in businesses incorporate some form of retrieval-augmented generation (RAG), with 30% utilizing multi-step chains or compound systems.

Rise of Compound Systems

A Compound AI System addresses AI tasks through multiple interconnected components, including several calls to different models, retrievers, or external tools. AI models are constantly improving, with scalability seemingly limitless. However, complex, multifaceted compound systems increasingly achieve the most advanced results. Combining the models with other components allows businesses to build dynamic systems that can address complex scenarios based on user queries at runtime, reduce model hallucinations, and increase user control and trust. Enterprises can design their compound systems based on their performance goals. E.g. In some applications, even the largest model may need to be more performant or too expensive. Still, an ensemble of smaller fine-tuned models augmented with optimized search and retrieve capabilities can give the best results. Github Copilot is an excellent example of this approach. While enterprises are making a shift in compounding AI systems, the emerging challenges are how to design, optimize & operate these systems. The compound systems consist of a data processing loop, query optimization loop, and operations management capabilities, and they can be independently optimized for better performance.

Karini AI Platform powered by AWS Gen AI for Compound AI Systems

AWS provides a broad set of Gen AI managed services such as Amazon Bedrock, Amazon SageMaker, and OpenSearch to build scalable generative AI applications. Amazon Bedrock is the most trusted and scalable fully managed service that offers a choice of high-performing foundation models from leading AI model providers and Amazon via a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.

Karini AI is a no-code Generative AI platform with a broad set of capabilities to build Compound AI systems purposefully built using AWS services to speed up production-grade application development. AWS customers can use best-of-breed capabilities to build production-grade RAG in a matter of minutes.

Data Processing Loop: Karini AI utilizes Amazon Textract and proprietary technologies to create LLM-ready data and provides built-in chunking algorithms. Customers can choose Amazon Bedrock hosted models or custom models hosted via Amazon SageMaker for chunking. Amazon OpenSearch delivers a secure and scalable vector store.

Query Optimization Loop: Karini AI employs the easy-to-use Prompt Playground to author, test, and compare the model performance of Bedrock-hosted models or custom models using Amazon SageMaker. Enterprises can leverage one of the many built-in chains, such as Q&A, summarization, classification, or Agentic workflows. Multiple ways are available to optimize retrieval using techniques such as query rewrite, query expansion, and context generation. Customers can also customize LLM-driven responses for greetings and follow-up questions.

Operations and Visibility: Karini AI provides built-in observability for tracing RAG chains and understanding low performing conversations. Copilot supports fine-grained feedback collection to gather user preferences and create instruction fine-tuning datasets. The built-in dashboards provide system performance and cost monitoring across model endpoints for Amazon Bedrock and SageMaker-hosted models. Karini AI provides enterprise connectors for significant number of data sources such as Amazon S3, Websites, Google Storage, Azure Storage, and Dropbox to unify data silos into a single vector store and also respects source system role-based access controls during serving.

Here is a quick end-2-end Karini AI Generative AI recipe powered by Amazon Bedrock models.

Conclusion:

Compound AI systems mark a significant advancement in AI technology by integrating various components to solve complex challenges that were once out of reach for traditional AI models. These systems are highly flexible, allowing for tailored responses and greater control over outputs. Karini AI’s advanced platform, coupled with Amazon Bedrock, enables the creation of sophisticated compound AI systems for any use case. By adopting these systems, businesses can enhance innovation, increase the quality and reliability of their AI solutions, and build stronger trust with their customers.

About Us: Fueled by innovation, we're making the dream of robust Generative AI systems a reality. No longer confined to specialists, Karini.ai empowers non-experts to participate actively in building/testing/deploying Generative AI applications. As the world's first GenAIOps platform, we've democratized GenAI, empowering people to bring their ideas to life – all in one evolutionary platform. Contact: Jerome Mendell (404) 891-0255 sales@karini.ai https://www.karini.ai/

[objectwaysblog]

Unleashing Hidden Data: Extracting Insights with Annotation Tools for PDFs 

In today's digital age, businesses generate and process a large volume of documents. These documents can be in a variety of formats, including PDFs, images, and scanned documents. Extracting data from these documents can be a challenge, but it is essential for many business processes. 

One way to extract data from documents is to use optical character recognition (OCR) software. OCR software can automatically read and convert text from images and scanned documents into a machine-readable format. However, OCR software is not always accurate, and it can be difficult to extract structured data from unstructured text. 

Another way to extract data from documents is to use a purpose-built annotation tool. Annotation tools allow users to highlight and annotate specific parts of a document. This can be helpful for identifying important information and for extracting structured data. 

There are a number of purpose-built annotation tools available, including Amazon Textract, Google Cloud Vision API, and Microsoft Azure Cognitive Services. These tools offer a variety of features, including the ability to extract text, identify entities, and extract tables. 

In addition to extracting data, annotation tools can also be used to improve the quality of OCR results. By highlighting and annotating specific parts of a document, users can help OCR software to better understand the context of the text. This can lead to more accurate OCR results. 

Annotation tools can be a valuable tool for extracting data from PDFs and other documents. By using a purpose-built annotation tool, businesses can improve the accuracy of their OCR results and extract structured data from unstructured text. 

Here are some of the benefits of using a purpose-built annotation tool: 

Improved accuracy of OCR results 

Ability to extract structured data from unstructured text 

Easier to identify important information 

Improved efficiency of document processing 

If you are looking for a way to extract data from PDFs and other documents, a purpose-built annotation tool is a good option. These tools can help you to improve the accuracy of your OCR results, extract structured data from unstructured text, and identify important information. 

Here are some of the steps involved in using a purpose-built annotation tool to extract data from PDFs: 

Upload the PDF to the annotation tool. 

Highlight and annotate the text that you want to extract. 

Save the annotated PDF. 

Run the OCR software on the annotated PDF. 

The OCR software will extract the text from the annotated PDF. 

Here are some of the considerations when choosing a purpose-built annotation tool: 

The features offered by the tool. 

The accuracy of the OCR results. 

The ease of use of the tool. 

The price of the tool. 

Summary:  

The blog provides a step-by-step guide on using a purpose-built annotation tool to extract data from PDFs, which includes uploading the PDF, highlighting and annotating the text, and running OCR software on the annotated PDF. 

Overall, purpose-built annotation tools offer improved accuracy of OCR results, the ability to extract structured data from unstructured text, ease of identifying important information, and increased efficiency in document processing. They prove to be a valuable solution for businesses seeking to enhance data extraction from PDFs and other documents. Contact us sales@objectways.com for more details.  

[ai-news]

In today’s business landscape, organizations are constantly seeking ways to optimize their financial processes, enhance efficiency, and drive cost savings. One area that holds significant potential for improvement is accounts payable. On a high leve #AI #ML #Automation

[psychicgalaxylove]

Amazon AWS Accounts with $5,000

Amazon AWS Accounts with $5,000 Credits

Available – Save Your Top Dollars on Amazon AWS Bills

 Join 1,000,000+ users tapping into the power of the most advanced

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 AWS Services You Can Avail $5,000 Credits On:

With these accounts, you can avail the following Amazon AWS services:

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Comprehend Medical

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 Services You Can’t Avail With These Credits:

While you can save your top dollars on almost all AWS services with these credits, it’s important to note that the Amazon $5,000 AWS credits can’t be applied to charges or fees for:

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 Exclusive Features:

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Note: These are rare accounts. And while we are loaded with many, they are limited in stock. So, if you are looking forward to saving your top dollars on Amazon AWS bills, grab these accounts before we run out of stock.

 Frequently Answered Questions:

·        What are Amazon AWS Credits?

AWS credits are credits that can be directly applied to bills to cover the costs associated with the eligible services. It’s one of the best ways of saving your top dollars while availing Amazon’s AWS services.

·        Can these $5,000 AWS credits be used with my current Amazon AWS account?

Yes – you can. Feel free to reach out to me for further details.

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Yes – they’ll expire in two year. You need to use them before they do.

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We are laser-focused on delivering the best quality service to our clients. These accounts are completely safe and just like any other AWS account. One thing you need to keep in mind is - you need to respect Amazon’s TOS.

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Yes – we are loaded with many Amazon AWS accounts. Feel free to reach out to us to discuss further details.

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These are standalone Amazon AWS accounts – not coupon codes.

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   Custom reviews

[ourwitching]

The task is to extract labels from US patent figures. Patent figures are different f...

[mindfiresolutions-blog]

Data Extraction for Healthcare Claims Processing

Executive Summary

The client wanted us to develop an application that would help them automate the high volume and arduous task of extracting data arriving from multiple sources and assist in claim processing. We built them a solution that extracts data from the ROI request forms, presents it to an operator for validation, and after doing so passes the results back to ROI Online through its API. Mindfire Solutions has implemented custom machine vision, natural language processing (NLP), and Robotic Process Automation (RPA) within the application to facilitate attended automation of the logging-and-assignment workflows. The logging-and-assignment application also enables the operator to pragmatically forecast the productivity gains that automation yields when deployed at scale on the Kubernetes cluster.

About our Client

Client : Confidential

Location: USA

Industry: Healthcare

Technologies

Python, Django, GraphQL, Vue.js, PWA, Azure, REST, Azure SQL, Google Vision, Amazon Textract, Solr, GIT, Docker, Kubernetes, Azure Key Vault, Azure BLOB storage, Jenkins

Download Full Case Study

[udauda]

TIL about AWS SAA

リソースグループ

Amazon EC2 インスタンス、AWS CloudFormation スタック、Amazon S3 バケットなどのリソースをまとめて管理���る機能。リソースグループとは、すべてが同じ AWS リージョンにあり、グループのクエリで指定された条件に適合する AWS リソースのコレクションのこと。グループを構築するために使用できる 2 つのタイプのクエリがある。

タグベース

AWS CloudFormation スタックベース

Lambda@Edge

CloudFront の機能で、アプリケーションユーザーに近いロケーションでコードを実行できるため、パフォーマンスが向上し待ち時間が短縮される。アプリケーションコードの変更なしに利用できる。

AWS Transfer Family

他のサーバーからS3 や EFS に SFTP、FTPS、FTP上でファイル転送を実行できるフルマネージド型のサービス。

Amazon Textract

印刷・手書きされた文書をPDFまたはJPG、PNG画像化し、テキスト・表からデータを抽出するサービス。日本語は非対応のため東京・大阪リージョンでの利用はできない。