Amazon Textract is a machine learning (ML) service that uses optical character recognition (OCR) to automatically extract text, handwriting, and data
Automatically extract printed text, handwriting, layout elements, and data from any document Drive higher business efficiency and faster decision-making while reducing costs. Extract key insights with high accuracy from virtually any document. Scale up or scale down the document processing pipeline to quickly adapt to market demands. Securely automate data processing with data privacy, encryption, and compliance standards. Accurately extract critical business data such as mortgage rates, applicant names, and invoice totals across a variety of financial forms to process loan and mortgage applications in minutes. Better serve your patients and insurers by extracting important patient data from health intake forms, insurance claims, and pre-authorization forms. Keep data organized and in its original context, and remove manual review of output. Easily extract relevant data from government-related forms, such as small business loans, federal tax forms, and business applications, with a high degree of accuracy. As part of the AWS Free Tier, you can get started with Amazon Textract for free. The Free Tier lasts for three months, and new AWS customers can analyze up to: Total pages processed = 100,000 Total pages processed = 2,000,000 Price per page = $0.0015 for first 1 million and $0.0006 for pages after 1 million Total pages processed = 5,000 pages Price for page with table = $0.015 Price for page with form (key-value pair) = $0.05 Price per page with Queries = $0.015 Total pages processed = 2,000,000 pages Price for page with Tables, Forms and Queries = $0.070 for the first one million and $0.055 for the next one million Let’s assume you want to extract data from 100,000 invoices using the Analyze Expense API. The pricing per page in the US West (Oregon) region for 1 million pages is $0.01 and you process 100,000 invoices. The total cost would be $1,000. See the calculation below: Total pages processed = 100,000 Let’s assume you want to extract data from 1,500,000 invoices using the Analyze Expense API. The pricing per page in the US West (Oregon) region for one million pages is $0.01 per page and $0.008 per page after one million. The total cost would be $14,000. See the calculation below: Total pages processed = 1,500,000 Price per page = $0.01 for the first 1 million and $0.008 for the next 500,000 Let’s say you want to extract information from 100,000 identity documents using the Analyze ID API. The pricing per page in the US West (Oregon) Region for 100,000 pages is $0.025 per page for up to 100,000 pages. The total cost would be $2,500. Total pages processed = 100,000 Let’s say you want to extract information from 600,000 identity documents using the Analyze ID API. The pricing per page in the US West (Oregon) Region for 100,000 pages is $0.025 per page and $0.01 per page after 100,000. The total cost would be $7,500. Total pages processed = 600,000 Let’s say you want to extract information from 200,000 pages of mort
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Pricing found: $0.0015,, $150., $0.0015, $0.0015, $150
Using AI to untangle 10,000 property titles in Latam, sharing our approach and wanting feedback
Hey. Long post, sorry in advance (Yes, I used an AI tool to help me craft this post in order to have it laid in a better way). So, I've been working on a real estate company that has just inherited a huge mess from another real state company that went bankrupt. So I've been helping them for the past few months to figure out a plan and finally have something that kind of feels solid. Sharing here because I'd genuinely like feedback before we go deep into the build. Context A Brazilian real estate company accumulated ~10,000 property titles across 10+ municipalities over decades, they developed a bunch of subdivisions over the years and kept absorbing other real estate companies along the way, each bringing their own land portfolios with them. Half under one legal entity, half under a related one. Nobody really knows what they have, the company was founded in the 60s. Decades of poor management left behind: Hundreds of unregistered "drawer contracts" (informal sales never filed with the registry) Duplicate sales of the same properties Buyers claiming they paid off their lots through third parties, with no receipts from the company itself Fraudulent contracts and forged powers of attorney Irregular occupations and invasions ~500 active lawsuits (adverse possession claims, compulsory adjudication, evictions, duplicate sale disputes, 2 class action suits) Fragmented tax debt across multiple municipalities A large chunk of the physical document archive is currently held by police as part of an old investigation due to old owners practices The company has tried to organize this before. It hasn't worked. The goal now is to get a real consolidated picture in 30-60 days. Team is 6 lawyers + 3 operators. What we decided to do (and why) First instinct was to build the whole infrastructure upfront, database, automation, the works. We pushed back on that because we don't actually know the shape of the problem yet. Building a pipeline before you understand your data is how you end up rebuilding it three times, right? So with the help of Claude we build a plan that is the following, split it in some steps: Build robust information aggregator (does it make sense or are we overcomplicating it?) Step 1 - Physical scanning (should already be done on the insights phase) Documents will be partially organized by municipality already. We have a document scanner with ADF (automatic document feeder). Plan is to scan in batches by municipality, naming files with a simple convention: [municipality]_[document-type]_[sequence] Step 2 - OCR Run OCR through Google Document AI, Mistral OCR 3, AWS Textract or some other tool that makes more sense. Question: Has anyone run any tool specifically on degraded Latin American registry documents? Step 3 - Discovery (before building infrastructure) This is the decision we're most uncertain about. Instead of jumping straight to database setup, we're planning to feed the OCR output directly into AI tools with large context windows and ask open-ended questions first: Gemini 3.1 Pro (in NotebookLM or other interface) for broad batch analysis: "which lots appear linked to more than one buyer?", "flag contracts with incoherent dates", "identify clusters of suspicious names or activity", "help us see problems and solutions for what we arent seeing" Claude Projects in parallel for same as above Anything else? Step 4 - Data cleaning and standardization Before anything goes into a database, the raw extracted data needs normalization: Municipality names written 10 different ways ("B. Vista", "Bela Vista de GO", "Bela V. Goiás") -> canonical form CPFs (Brazilian personal ID number) with and without punctuation -> standardized format Lot status described inconsistently -> fixed enum categories Buyer names with spelling variations -> fuzzy matched to single entity Tools: Python + rapidfuzz for fuzzy matching, Claude API for normalizing free-text fields into categories. Question: At 10,000 records with decades of inconsistency, is fuzzy matching + LLM normalization sufficient or do we need a more rigorous entity resolution approach (e.g. Dedupe.io)? Step 5 - Database Stack chosen: Supabase (PostgreSQL + pgvector) with NocoDB on top Three options were evaluated: Airtable - easiest to start, but data stored on US servers (LGPD concern for CPFs and legal documents), limited API flexibility, per-seat pricing NocoDB alone - open source, self-hostable, free, but needs server maintenance overhead Supabase - full PostgreSQL + authentication + API + pgvector in one place, $25/month flat, developer-first We chose Supabase as the backend because pgvector is essential for the RAG layer (Step 7) and we didn't want to manage two separate databases. NocoDB sits on top as the visual interface for lawyers and data entry operators who need spreadsheet-like interaction without writing SQL. Each lot becomes a single entity (primary key) with relational links to: contracts, bu
View originalBest OCR for template-based form extraction? [D]
Hi, I’m working on a school project and I’m currently testing OCR tools for forms. The documents are mostly structured or semi-structured forms, similar to application/registration forms with labeled fields and sections. My idea is that an admin uploads a template of the document first, then a user uploads a completed form, and the system extracts the data from it. After extraction, the user reviews the result, checks if the fields are correct, and edits anything that was read incorrectly. So I’m looking for an OCR/document understanding tool that can work well for template-based extraction, but also has some flexibility in case document layouts change later on. Right now I’m trying Google Document AI, and I’m planning to test PaddleOCR next. I wanted to ask what OCR tools you’d recommend for this kind of use case. I’m mainly looking for something that: works well on scanned forms can map extracted text to the correct fields is still manageable if templates/layouts change is practical for a student research project If you’ve used Document AI, PaddleOCR, Tesseract, AWS Textract, Azure AI Document Intelligence, or anything similar for forms, I’d really appreciate your thoughts. submitted by /u/Sudden_Breakfast_358 [link] [comments]
View originalYes, Textract offers a free tier. Pricing found: $0.0015,, $150., $0.0015, $0.0015, $150
