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Apryse
Apryse
@apryse.bsky.social
2 days ago
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if 95% of organizations are confident in their #AI #document pipelines, why do more than half of those same organizations report frequent quality failures?

Get the full breakdown in our AI Readiness 2025 Addendum: bit.ly/4sBYSmT

#AI #DocumentProcessing #DataQuality

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Compass365
Compass365
@compass365.bsky.social
3 days ago
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Document processing for Microsoft 365 is taking the place of SharePoint Premium. Learn what has changed and how this will impact SharePoint Premium users: buff.ly/a4aWmD5
#documentprocessing #Microsoft365Copilot

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Compass365
Compass365
@compass365.bsky.social
3 weeks ago
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Document processing for Microsoft 365 is taking the place of SharePoint Premium. Learn what has changed and how this will impact SharePoint Premium users: buff.ly/a4aWmD5
#documentprocessing #Microsoft365Copilot

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Mavenyx
Mavenyx
@mavenyx.bsky.social
1 month ago
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📑🔍🔀What is OCR & How it works.
www.mavenyx.com/blog/ocr/wha... #OCR #IDP #AI #DocumentProcessing #DataExtraction

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DocumentExtraction
DocumentExtraction
@dataextraction.bsky.social
1 month ago
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📑🔍🔀What is OCR & How it works.

www.mavenyx.com/blog/ocr/wha... #OCR #IDP #AI #DocumentProcessing #DataExtraction

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AdwaitX
AdwaitX
@adwaitx.bsky.social
1 month ago
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AI Agents Are Turning Documents Into Real-Time Business Intelligence, Here’s How Key Takeaways 63% of Fortune 250 companies deployed intelligent document processing solutions, with financial sector leading at 71% adoption AI-powered IDP market valued between $2.3 billion and $10.57 billion in 2024-2025, projected to reach $91 billion by 2034 NVIDIA Nemotron hybrid Mamba-Transformer architecture delivers 35% higher throughput in multi-page document processing Organizations automate document workflows using AI agents that extract tables, charts, and text while maintaining citation transparency…

AI agents extract business intelligence from complex documents in seconds 63% of Fortune 250 companies deployed these systems. AdwaitX reveals how NVIDIA Nemotron transforms document processing with 35% faster throughput. #AdwaitX #AIAgents #DocumentProcessing

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Mark Jack
Mark Jack
@markjackmilian.bsky.social
1 month ago
Just a moment...

Looking for budget-friendly alternatives to Aspose for document processing in Excel, PDF, Word? Discover API solutions offering robust features without high costs. Streamline your workflows with efficient toolsets. #DocumentProcessing #APISolutions

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HitechBPO
HitechBPO
@hitechbpo.bsky.social
1 month ago
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How should real estate companies enhance document processing?

Learn practical strategies to streamline real estate document processing and stay competitive: hitechbpo.medium.com/how-should-r...

#realestatecompanies #documentprocessing

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Compass365
Compass365
@compass365.bsky.social
1 month ago
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Document processing for Microsoft 365 is taking the place of SharePoint Premium. Learn what has changed and how this will impact SharePoint Premium users: buff.ly/a4aWmD5
#documentprocessing #Microsoft365Copilot

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Mavenyx
Mavenyx
@mavenyx.bsky.social
1 month ago
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📊 📄🔍Structured vs unstructured data. Understand the key differences and discover how document processing systems handle each to deliver reliable business insights. www.mavenyx.com/structured-v... #StructuredData #UnstructuredData #DocumentProcessing #IntelligentDocumentProcessing #OCRTechnology

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Compass365
Compass365
@compass365.bsky.social
2 months ago
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Document processing for Microsoft 365 is taking the place of SharePoint Premium. Learn what has changed and how this will impact SharePoint Premium users: buff.ly/a4aWmD5
#documentprocessing #Microsoft365Copilot

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Compass365
Compass365
@compass365.bsky.social
2 months ago
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Document processing for Microsoft 365 is taking the place of SharePoint Premium. Learn what has changed and how this will impact SharePoint Premium users: buff.ly/a4aWmD5
#documentprocessing #Microsoft365Copilot

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Compass365
Compass365
@compass365.bsky.social
3 months ago
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Document processing for Microsoft 365 is taking the place of SharePoint Premium. Learn what has changed and how this will impact SharePoint Premium users: buff.ly/a4aWmD5
#documentprocessing #Microsoft365Copilot

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Vlad Catrinescu
Vlad Catrinescu
@vladtalkstech.com
3 months ago
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📄 Autofill Columns + Managed Metadata = Perfectly Tagged SharePoint Libraries! 🤖

#SharePoint #Microsoft365 #ContentAI #AutofillColumns #ManagedMetadata #DocumentProcessing #SharePointOnline #Microsoft365Tips #AITools #Governance #ProductivityTips

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HitechBPO
HitechBPO
@hitechbpo.bsky.social
3 months ago
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Get Professional Real Estate Document Processing for 100% Compliance

Streamline operations, boost productivity, and stay future-ready with accurate and structured document management.

Learn more: www.hitechbpo.com/real-estate-...

#realestate #documentprocessing #propertytech

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Compass365
Compass365
@compass365.bsky.social
3 months ago
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Document processing for Microsoft 365 is taking the place of SharePoint Premium. Learn what has changed and how this will impact SharePoint Premium users: buff.ly/y2ks4h3
#documentprocessing #Microsoft365Copilot

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Compass365
Compass365
@compass365.bsky.social
4 months ago
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Microsoft has rebranded SharePoint Premium yet again to document processing for Microsoft 365. Learn what has changed and how this will impact SharePoint Premium users: buff.ly/yRQNSGo
#documentprocessing #Microsoft365Copilot

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Vlad Catrinescu
Vlad Catrinescu
@vladtalkstech.com
4 months ago
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🚀 Microsoft’s New Focus for Document Processing in M365 Revealed! 📄

Give the full video a watch here 👇
www.youtube.com/watch?v=h8vD...

#SharePoint #Microsoft365 #DocumentProcessing #ContentAI #MicrosoftSyntex #Automation #AITools #SharePointOnline #Microsoft365Tips #ProductivityTips

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Vlad Catrinescu
Vlad Catrinescu
@vladtalkstech.com
4 months ago
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📂 SharePoint Premium Split Explained — What’s Under Document Processing for M365! 💡

#SharePoint #Microsoft365 #DocumentProcessing #ContentAI #MicrosoftSyntex #SharePointOnline #Microsoft365Tips #AITools #ProductivityTips

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@marcor73.bsky.social
5 months ago
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eSignature now available worldwide | Microsoft Community Hub We’re excited to share that eSignature for Microsoft 365 is now available worldwide* on Microsoft 365 public clouds. This pertains to all PDFs and Word...

📢 📢 📢 📢 Another step into the future

eSignature now available worldwide

#eSignature #MicrosoftWord #DocumentProcessing #Microsoft365

techcommunity.microsoft.com/blog/sharepo...

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Vlad Catrinescu
Vlad Catrinescu
@vladtalkstech.com
6 months ago
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How to Know If SharePoint AI Got It Right ✅ (New Processing Status Feature)

#SharePoint #ContentAI #MetadataExtraction #AIBuilder #Microsoft365 #DocumentProcessing #SharePointAI #Shorts @xgokan.bsky.social

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DocumentExtraction
DocumentExtraction
@dataextraction.bsky.social
7 months ago
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Manual data extraction in real estate is slow and error-prone. AI-powered IDP makes it faster, smarter, and accurate. Discover how 👉 www.algodocs.com/intelligent-...
#RealEstateAutomation #AIinRealEstate #DataExtraction #DocumentProcessing #AlgoDocs

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@urvishah.bsky.social
7 months ago
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Transform Manufacturing Operations with Document Processing System Did you know 70% of manufacturing delays stem from manual document handling? As manufacturers push toward efficiency and innovation, digital transformation in manufacturing has become more critical th...

🏭 Digital Document Processing in Manufacturing

Streamline your manufacturing ops with smart document processing for speed, accuracy, and compliance.

👉 www.writerinformation.com/insights/tra...

manufacturing automation, digital workflows

#WriterInformation #ManufacturingTech #DocumentProcessing

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Reworked
Reworked
@reworked.bsky.social
8 months ago
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Hyland Unveils Agentic AI-Powered Document Processing Solution Content management provider introduces autonomous document processing technology that aims to transform enterprise workflows through generative AI capabilities. Continue reading...

#AI #DocumentProcessing

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Naoya
Naoya
@naoyacreates.bsky.social
9 months ago
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Docling: Open-Source Document Processing Toolkit for AI | AI News Transform documents for AI with Docling, an open-source toolkit from IBM Research! Parse PDFs, spreadsheets, & more for efficient AI workflows.

AIMindUpdate News!
Want to speed up your AI projects by 30x? Docling, the open-source toolkit, transforms documents into AI-ready data! #Docling #AI #DocumentProcessing

Click here↓↓↓
aimindupdate.com/2025/05/30/d...

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Citizen Portal News Oklahoma
Citizen Portal News Oklahoma
@citizenptnewsok.bsky.social
9 months ago
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Oklahoma Secretary of State sets new fees for financing statements and document requests Secretary of State outlines fees for filings and document processing in Oklahoma.

Oklahoma's Secretary of State just announced new fees for financing statements and document requests—find out how it could impact you!

Learn more here

#OK #PublicFees #CitizenPortal #GovernmentTransparency #DocumentProcessing

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DocumentExtraction
DocumentExtraction
@dataextraction.bsky.social
10 months ago
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🤖📜🔍Here are the best large language models (LLMs) for basic document processing in 2025. Read our guide to learn more: www.algodocs.com/best-llm-mod...
#AI #MachineLearning #LLM #DocumentProcessing #TechTrends2025 #AIModels #WorkflowAutomation #ArtificialIntelligence #AlgoDocs #Chatgpt #Gemini

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Forem
Forem
@forem.com.web.brid.gy
10 months ago
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Local PDF Parsing with AWS Textract & Python (Part 1) # ✍️ Introduction Throughout my experience working with clients from domains like `healthcare`, `insurance`, and `legal`, I often found myself curious about how certain backend document workflows functioned, especially in healthcare. While supporting these systems, I’d often get paged for incidents related to PDF pipelines: upload failures, script errors, or extraction gaps. At that stage, like many in support roles, we’re limited to handling outcomes rather than building or understanding the full solution. Over time, as we gain more experience, build trust, and make people feel confident in our abilities, we gradually get the opportunity to be part of architecture discussions and solution design conversations. But that curiosity about how these pipelines actually work — from PDF upload to raw text extraction — always stayed with me. So I decided to finally explore this from scratch, hands-on, and document it as a small weekend project. This repository reflects that journey — one that started with a question and ended with deeper insights, hands-on practice, and a working prototype. My hope is that others who share this curiosity will find this just as helpful. ## 🔍 What This Project Is This project focuses on extracting structured data from scanned or uploaded PDFs using `AWS Textract`, starting with a local Python-based flow. It simulates real-world use cases commonly seen in **healthcare** , **legal** , and **insurance** sectors — where physical documents like visit summaries or forms need to be digitized and stored in structured formats like databases. The goal? To break down what typically happens behind the scenes — from raw scanned input to clean, queryable output — using AWS-native services. ## 📄 Why Document Parsing Matters In many industries, large volumes of information are still locked inside unstructured files, like PDFs or images. For example: * A **hospital** stores patient visit summaries scanned from handwritten or printed forms. * An **insurance company** receives thousands of claim forms uploaded as PDFs every week. * A **legal team** scans documents, contracts, and evidence that need to be searchable. Without parsing, this data remains buried and unusable. Document parsing — especially automated parsing — allows organizations to: * Extract critical fields (like patient name, ID, diagnosis) * Store them in structured systems (like `DynamoDB`) * Enable downstream use (dashboards, alerts, summaries, etc.) This project is a hands-on way to explore how that all comes together. ## 🧪 Local First: Why I Didn’t Start with Automation While it’s tempting to jump straight into Lambda functions and triggers, I deliberately started with a **local-first mindset**. Why? * It helps build intuition: you understand exactly what Textract returns and how the parsing logic works. * Easier to test and debug before handing things off to automation. * You stay in control, tweaking and improving the logic before putting it behind an event trigger. This mirrors how real-world teams prototype internally before scaling. In my case: * Took a sample patient visit summary in PDF format. * Wrote a simple `Python` script to call `AWS Textract`. * Parsed the returned lines into structured fields. * The script automatically saved the extracted text as a `.txt` file inside `output-texts/`. I opened it to manually check if Textract returned the expected content. That local foundation made automation smoother and more predictable. # 🧱 Prerequisites To follow along or replicate this project, ensure the following are in place: * An `AWS` account (root access only used for visual verification) * A dedicated `IAM` user with the following permission: * `AmazonTextractFullAccess` * `AWS CLI` installed and configured with the IAM user credentials * `Python` 3.9+ installed * `virtualenv` installed * `VS Code` (or any preferred IDE) You should also: * Create a virtual environment (`python -m venv venv`) and activate it. * Install boto3 (`pip install boto3`) and freeze dependencies into requirements.txt ## 📂 Project Structure pdf-to-text-extractor/ ├── input-pdfs/ # Local test PDFs ├── output-texts/ # Extracted raw text output ├── scripts/ # Python scripts │ └── extract_textract.py ├── venv/ # Virtual environment (ignored in git) ├── requirements.txt ├── .gitignore └── README.md # Project Documentation Why `venv` and `requirements.txt` matter: * Using a `venv/` keeps dependencies isolated — it’s a clean, repeatable habit in Python workflows. * The `requirements.txt` file lists all the packages I used, so anyone can recreate the same environment instantly. ## 🧪 What the Local Script Does In this phase, wrote a simple Python script to: * Load a PDF from the `input-pdfs/` folder * Send it to `Textract` for text extraction * Save the output to `output-texts/` as a `.txt` file This helped validate if Textract could read and extract meaningful content, before jumping into parsing or automation. ## ✅ Outcome and What’s Next By the end of this phase, I had a working local prototype that: * Pulled a PDF from `input-pdfs/` * Extracted raw text using `AWS Textract` * Saved it to `output-texts/` * Gave me a chance to test and fine-tune the logic manually This local-first phase gave me the space to deeply understand what each piece does before scaling up. ## 🔗 References * Python `boto3` Documentation * Amazon Textract Documentation * Setting up a Python Virtual Environment * What is `requirements.txt` 📂 **Explore the Full Codebase** All the files used in this local setup are available here: 🔗 GitHub Repo → pdf-to-text-extractor/local ### 🔜 Coming Up in Part 2: We’ll build on this by: * Triggering Textract via **AWS Lambda** * Parsing and storing results in **DynamoDB** * We’ll automate everything after the upload — using **AWS services** to handle extraction, parsing, and storage — just like a real-world backend system would. 📘 _Stay tuned for Part 2: “Building a Serverless PDF Ingestion Flow”_
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AWS Community Builder Blog Posts
AWS Community Builder Blog Posts
@awscmblogposts.bsky.social
10 months ago
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Local PDF Parsing with AWS Textract & Python (Part 1) ✍️ Introduction Throughout my experience working with clients from domains like...

✍️ New blog post by Sandeep Sangu

Local PDF Parsing with AWS Textract & Python (Part 1)

#python #aws #textract #documentprocessing

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