Intelligent Word Recognition helps improve handwriting recognition accuracy by matching written responses against your predefined dictionary of expected answers. This is especially useful for questions with predictable responses like city names, course titles, or product names.

How IWR improves accuracy

Unlike standard character-by-character recognition, IWR matches entire words against your dictionary. For example:

• Standard recognition might read "New Yrok" (character error)
• IWR matches it to "New York" from your dictionary
• The correct answer is automatically verified

When to use IWR

IWR works best for questions with:

• Limited possible answers - Countries, states, cities
• Repeated responses - Course names, department names
• Common misspellings - Technical terms, brand names
• Standardized formats - ID numbers, codes

Setting up IWR

Step 1: Navigate to Dictionary settings

1. Go to Surveys in your dashboard
2. Select your survey
3. Click the Verify tab
4. Select Dictionary

Step 2: Add verification rules

1. Click Add Custom Rule
2. Select the question (works with Short Text and Number questions)
3. Choose your matching method:
   • Text is similar to - Recommended for handwritten text
   • Text is equal to - Best for numbers or codes

Step 3: Enter expected values

Add your dictionary entries, one per line:

Example entries:

New York
Los Angeles  
Chicago
Houston
Phoenix

Click Add to save your rule.

Best practices

For text responses

• Use "Text is similar to" for flexibility
• Include common variations (NY, New York, NYC)
• Add common misspellings you've observed
• Keep lists manageable (under 100 entries)

For numeric responses

• Use "Text is equal to" for exact matching
• Include formats with/without separators
• Add leading zeros if applicable
• Consider ranges for validation

Dictionary management tips

• Start with a small dictionary and expand based on actual responses
• Review unmatched responses regularly
• Export verified responses to identify new dictionary entries
• Group similar rules by question type

How IWR works with handwriting recognition

PaperSurvey uses advanced Handwriting Text Recognition (HTR) powered by neural networks. IWR enhances this by:

1. Processing the handwritten text through HTR first
2. Comparing results against your dictionary
3. Auto-correcting close matches
4. Flagging uncertain matches for review

Common use cases

Location questions

United States
United Kingdom
Canada
Australia
Germany

Course evaluations

Introduction to Psychology
Calculus I
Organic Chemistry
World History
English Literature

Product feedback

Model A-100
Model A-200
Model B-100
Premium Version
Standard Version

Monitoring and improvement

Review verification results

• Check the Verify tab regularly
• Look for patterns in unmatched responses
• Add frequently occurring responses to your dictionary

Optimize your rules

• Remove rarely used entries
• Combine similar variations
• Adjust matching methods based on accuracy

Troubleshooting

Issue: Too many false matches Solution: Switch from "similar to" to "equal to" for stricter matching

Issue: Valid responses not matching Solution: Add more variations to your dictionary

Issue: Numbers not matching correctly Solution: Ensure format consistency in your dictionary

IWR significantly reduces manual verification time while maintaining data accuracy. Start with common responses and refine your dictionary as you collect more data.