MahaRERA Data Extraction & Processing

@farhanyoussef18

Youssef, Full-Time Python Developer specializing in data extraction and AI automation. Your goal to extract data from 48,000 MahaRERA URLs, process PDFs, and perform AI analysis is clear. My approach uses Playwright for robust navigation and captcha handling, Scrapy for efficient data extraction, and a custom AI/NLP stack for analyzing Marathi/English documents to categorize deal structures. I have successfully completed similar large-scale data extraction and document processing projects. To proceed with the proof of concept, could you confirm the specific data points you need from the HTML tables beyond those listed? Ready to start immediately.

@vishal1145

MahaRERA - Your pipeline will fail if the captcha solver cannot maintain session persistence across 48,000 requests. Most OCR tools break after 500-1000 consecutive attempts because the portal starts serving distorted images or rate-limiting IPs. I've built similar extraction systems for legal document processing at scale, and the real bottleneck isn't the scraping - it's ensuring your NLP model can accurately parse unstructured Marathi legal clauses where "consideration" is buried in 40-page deeds using inconsistent terminology. Before architecting the solution, I need clarity on two things: First, what's your acceptable error rate for the AI extraction? If the model misclassifies 5% of deal structures as "Mixed" when they're actually "Revenue Share," does that break your downstream analysis? Second, are these PDFs searchable text or scanned images? If they're image-based, we'll need OCR preprocessing before NLP, which adds 3-4 seconds per document and changes the timeline significantly. Here's the architectural approach: - CAPTCHA BYPASS: Deploy rotating residential proxies with Tesseract OCR fine-tuned on MahaRERA's font patterns, maintaining session cookies to avoid detection across batch requests. - PDF PROCESSING: Use PyPDF2 for merging multi-part files with error handling for corrupted downloads, then validate file integrity before naming convention application. - NLP STACK: Fine-tune a multilingual BERT model (mBERT or IndicBERT) on Marathi legal corpus to extract consideration clauses, then use GPT-4 API for structured summarization into your 5 categories with confidence scoring. - DATA VALIDATION: Implement checksum verification on all 48K records to flag missing fields, duplicate registration numbers, or failed PDF merges before final delivery. I've processed 120K+ legal documents for a proptech client where accuracy was non-negotiable - we achieved 94% classification accuracy on deal structures after two rounds of model retraining. I don't take on projects where the NLP requirements are vague. Let's discuss edge cases like handwritten annotations in scanned deeds and how you want the system to handle extraction failures before I commit to a fixed timeline.

@muneeb6767

I’ll automate this end-to-end using Python (Playwright + asyncio + pandas + OpenCV/Tesseract + LLM API) to reliably handle CAPTCHA, scraping, PDF merging, and AI extraction at scale. I’ve built similar 50k+ record pipelines with OCR + multilingual NLP (English/Marathi via OpenAI/Claude + Indic models) ensuring structured outputs and high accuracy.

@akkiwadhwa1995

Hi, I can deliver the complete DaaS pipeline for 48,000 MahaRERA URLs including captcha handling, structured data extraction, PDF merging, and AI-based deal analysis. Approach: Scalable scraping pipeline with session handling + OCR-based captcha solving Distributed workers (Python, asyncio/queue-based) to process high volume reliably Automated PDF download, stitching, and standardized naming AI/NLP layer using LLMs (GPT + Indic language support) with preprocessing (OCR cleanup, Marathi→English normalization) Classification + structured extraction for “Consideration Type” and concise summaries Data validation + deduplication to ensure clean final dataset Deliverables: Clean CSV/XLSX with all required fields + AI outputs Zipped folder of merged, correctly named PDFs Milestone 1: 100 URLs fully processed for validation Milestone 2: Full dataset delivery Estimate: Cost: ₹28,000 INR Timeline: POC (100 URLs): 2–3 days Full delivery: 8–10 days after approval AI/NLP Stack: OCR + text normalization for scanned Marathi/English docs LLM-based extraction (prompt + schema guided) Classification layer for deal types (Monetary, Barter, Revenue Share, etc.) Post-processing rules to improve accuracy and consistency I have experience building large-scale scraping + AI enrichment pipelines (millions of records), so this can be delivered reliably with high accuracy. Ready to begin with the 100-URL milestone immediately.