A channel layer that turns physical documents into trustworthy digital data — OCR + Markdown + metadata + optional field extraction, exposed via REST / EventBus / MCP / Webhook to downstream RAG platforms, business systems, and AI clients. Built on ABP.

Professional document scanner built with vanilla JavaScript. Scan documents using your device camera, auto A4 PDF export, smooth animation, corner markers guide, mobile responsive. No external frameworks needed.

Source code from my college thesis

FRA Atlas AI is an intelligent forest rights digitization and monitoring system that automates document verification via OCR, classifies land using CNN & ISRO Bhuvan data, and powers a LangChain-based Decision Support System for transparent, data-driven governance.

Multi-stage deep learning pipeline that parses scanned handwritten calendars into structured data — segmenting regions, classifying day/month cells, and recognizing handwritten annotations.

AI-powered document scanner that automatically detects, corrects perspective, and enhances scanned documents from photos using OpenCV

OCR/HTR web service for digitizing Moscow church records (18-19th century). Extracts structured data (names, dates, addresses) from historical documents with NER and WER quality metrics. Hackathon project for Moscow Main Archive.

Multi-stage OCR pipeline for 16th–19th century handwritten Spanish manuscripts using fine-tuned Kraken HTR and GLM-4V ensemble recognition

"Interactive OCR system with real-time correction using Tesseract and confidence-based filtering."

An idempotent, async ETL pipeline for extracting structured data from image-based forms — built with Playwright, OpenCV, and Gemini Batch API, with built-in cost tracking.