🤖 ML Project
NLP Document Classification & Entity Extraction API
NLP microservice classifying PDFs and extracting key entities.
Project Overview
Internal Project
NLP / Document Processing
2 months (2024)
ML Engineer
Built an NLP microservice that automatically classifies documents and extracts structured data from unstructured PDFs.
The Challenge
Manual document processing was a bottleneck:
Hours spent manually categorizing documents
Inconsistent classification across team members
Key data buried in unstructured text
No searchable document database
Compliance risks from misfiled documents
They needed automated document understanding.
My Solution
I developed an intelligent document processing system:
Text Extraction
OCR and PDF parsing for text extraction from any document.
Classification Model
Fine-tuned transformer model for document type classification.
Entity Extraction
spaCy NER for extracting names, dates, amounts, and custom entities.
REST API
FastAPI endpoint for document upload and processing.
Key Features
PDF Processing
Extract text from scanned and digital PDFs.
Classification
Auto-categorize into 20+ document types.
Entity Extraction
Extract dates, names, amounts, and more.
API Ready
RESTful API for easy integration.
Tech Stack
NLP
API
Document
ML
Screenshots
Entity Extraction
API Documentation
Classification Results
Results & Impact
95%
Accuracy
Classification
20+
Document
Types
2sec
Processing
Per Doc
10K+
Documents
Processed
Key Achievements
95% classification accuracy across 20+ document types
Processing time under 2 seconds per document
Extracted 50+ entity types with high precision
Processed 10,000+ documents in production
Interested in Something Similar?
I help businesses build robust backend systems, membership platforms, and automation tools.
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