How to Build an AI-Powered OCR Document Processor

This ai ocr document processor extracts text and structured data from scanned documents and images using automated extraction. Paper documents become searchable data.

What You Need Before Starting

• Python 3.8+
• Claude or GPT API key
• python-docx or PyPDF2 installed
• Storage system (local or S3)

Step 1: Set Up Document Processing

Build the foundation for ocr.

import os
import json
from datetime import datetime

def init_doc_system(base_dir):
    os.makedirs(os.path.join(base_dir, "inbox"), exist_ok=True)
    os.makedirs(os.path.join(base_dir, "processed"), exist_ok=True)
    os.makedirs(os.path.join(base_dir, "archive"), exist_ok=True)

    import sqlite3
    conn = sqlite3.connect(os.path.join(base_dir, "documents.db"))
    conn.execute("""CREATE TABLE IF NOT EXISTS documents (
        id INTEGER PRIMARY KEY AUTOINCREMENT,
        filename TEXT, doc_type TEXT, status TEXT,
        metadata TEXT, processed_at TEXT
    )""")
    conn.commit()
    return conn

Step 2: Process Documents

Extract content and metadata from incoming documents.

def process_document(file_path):
    ext = os.path.splitext(file_path)[1].lower()
    if ext == ".pdf":
        import PyPDF2
        with open(file_path, "rb") as f:
            reader = PyPDF2.PdfReader(f)
            text = "\n".join(page.extract_text() for page in reader.pages)
    elif ext == ".docx":
        import docx
        doc = docx.Document(file_path)
        text = "\n".join(p.text for p in doc.paragraphs)
    else:
        with open(file_path) as f:
            text = f.read()
    return {"filename": os.path.basename(file_path), "text": text, "ext": ext}

Step 3: Analyze with AI

Use Claude to classify, tag, or review the document.

import anthropic

def analyze_document(doc_data):
    client = anthropic.Anthropic()
    message = client.messages.create(
        model="claude-sonnet-4-20250514", max_tokens=1500,
        messages=[{"role": "user",
            "content": f"Analyze this document and provide: 1) Document type 2) Key data points 3) Summary\n\n{doc_data['text'][:3000]}"}])
    return message.content[0].text

Step 4: Route and Store

Move processed documents to the right location.

import shutil

def route_document(doc_data, analysis, base_dir, conn):
    doc_type = extract_type(analysis)
    dest_dir = os.path.join(base_dir, "processed", doc_type)
    os.makedirs(dest_dir, exist_ok=True)

    dest_path = os.path.join(dest_dir, doc_data["filename"])
    shutil.move(doc_data["original_path"], dest_path)

    conn.execute(
        "INSERT INTO documents (filename, doc_type, status, metadata, processed_at) VALUES (?, ?, ?, ?, ?)",
        (doc_data["filename"], doc_type, "processed", analysis, datetime.now().isoformat()))
    conn.commit()

Step 5: Monitor and Report

Track processing stats and flag issues.

def daily_report(conn):
    stats = conn.execute("""
        SELECT doc_type, COUNT(*), MAX(processed_at)
        FROM documents WHERE processed_at >= date('now', '-1 day')
        GROUP BY doc_type
    """).fetchall()

    report = "Daily Document Processing:\n"
    for doc_type, count, last in stats:
        report += f"  {doc_type}: {count} documents (last: {last})\n"
    return report

What to Build Next

Add structured extraction. Pull tables, dates, and amounts from scans.

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