PDF OCR Accuracy Checklist: Get Cleaner Text

Quick answer: OCR accuracy is mostly determined by input quality. For the best results: 300 DPI, high contrast, correct language, straight pages, and quick spot checks. Run OCR at /pdf-ocr.

• Use 300 DPI for typical documents.
• Keep pages flat and aligned; avoid shadows and skew.
• If the scan is blurry, rescan if possible (software cannot fully recover lost detail).

Key takeaways

• Definition: Start with the scan explains what you are looking at and why it matters in practice.
• Context: this section helps you interpret inputs and outputs correctly, not just run a tool.
• Verification: confirm assumptions (format, encoding, units, or environment) before changing anything.
• Consistency: apply one approach end-to-end so results are repeatable and easy to debug.

Common pitfalls

• Mistake: skipping validation and trusting the first output you see from Start with the scan.
• Mistake: mixing formats or layers (for example, decoding the wrong field or using the wrong unit).

Quick checklist

1. Identify the exact input format and whether it is nested or transformed multiple times.
2. Apply the minimal transformation needed to make it readable.
3. Validate the result (structure, encoding, and expected markers).
4. If the result still looks encoded, repeat step-by-step and stop as soon as it becomes clear.

• Increase contrast if the background is gray.
• Crop heavy borders and scanner shadows.
• Rotate to the correct orientation (sideways pages reduce accuracy).
• If the document is color-heavy, try grayscale for clearer text edges.

Key takeaways

• Definition: Improve readability before OCR explains what you are looking at and why it matters in practice.
• Context: this section helps you interpret inputs and outputs correctly, not just run a tool.
• Verification: confirm assumptions (format, encoding, units, or environment) before changing anything.
• Consistency: apply one approach end-to-end so results are repeatable and easy to debug.

Common pitfalls

• Mistake: skipping validation and trusting the first output you see from Improve readability before OCR.
• Mistake: mixing formats or layers (for example, decoding the wrong field or using the wrong unit).

Quick checklist

1. Identify the exact input format and whether it is nested or transformed multiple times.
2. Apply the minimal transformation needed to make it readable.
3. Validate the result (structure, encoding, and expected markers).
4. If the result still looks encoded, repeat step-by-step and stop as soon as it becomes clear.

OCR engines need a language model. If you pick the wrong one, letters and punctuation are the first to degrade.

Key takeaways

• Definition: Choose the right language(s) explains what you are looking at and why it matters in practice.
• Context: this section helps you interpret inputs and outputs correctly, not just run a tool.
• Verification: confirm assumptions (format, encoding, units, or environment) before changing anything.
• Consistency: apply one approach end-to-end so results are repeatable and easy to debug.

Common pitfalls

• Mistake: skipping validation and trusting the first output you see from Choose the right language(s).
• Mistake: mixing formats or layers (for example, decoding the wrong field or using the wrong unit).

Quick checklist

1. Identify the exact input format and whether it is nested or transformed multiple times.
2. Apply the minimal transformation needed to make it readable.
3. Validate the result (structure, encoding, and expected markers).
4. If the result still looks encoded, repeat step-by-step and stop as soon as it becomes clear.

Tables often fail due to column structure. If you only need a few fields, consider extracting those regions and verifying manually.

Key takeaways

• Definition: Handle tables and forms carefully explains what you are looking at and why it matters in practice.
• Context: this section helps you interpret inputs and outputs correctly, not just run a tool.
• Verification: confirm assumptions (format, encoding, units, or environment) before changing anything.
• Consistency: apply one approach end-to-end so results are repeatable and easy to debug.

Common pitfalls

• Mistake: skipping validation and trusting the first output you see from Handle tables and forms carefully.
• Mistake: mixing formats or layers (for example, decoding the wrong field or using the wrong unit).

Quick checklist

1. Identify the exact input format and whether it is nested or transformed multiple times.
2. Apply the minimal transformation needed to make it readable.
3. Validate the result (structure, encoding, and expected markers).
4. If the result still looks encoded, repeat step-by-step and stop as soon as it becomes clear.

If accuracy is poor, re-run OCR after fixing one thing at a time:

• rotation/deskew
• contrast
• language selection
• cropping borders

Key takeaways

• Definition: Fast “re-run” rules explains what you are looking at and why it matters in practice.
• Context: this section helps you interpret inputs and outputs correctly, not just run a tool.
• Verification: confirm assumptions (format, encoding, units, or environment) before changing anything.
• Consistency: apply one approach end-to-end so results are repeatable and easy to debug.

Common pitfalls

• Mistake: skipping validation and trusting the first output you see from Fast “re-run” rules.
• Mistake: mixing formats or layers (for example, decoding the wrong field or using the wrong unit).

Quick checklist

1. Identify the exact input format and whether it is nested or transformed multiple times.
2. Apply the minimal transformation needed to make it readable.
3. Validate the result (structure, encoding, and expected markers).
4. If the result still looks encoded, repeat step-by-step and stop as soon as it becomes clear.

Spot-check:

• headings
• totals and numbers
• dates and IDs
• names and addresses

If those look right, most of the document is usually fine.

Key takeaways

• Definition: Verify the output (fast, effective) explains what you are looking at and why it matters in practice.
• Context: this section helps you interpret inputs and outputs correctly, not just run a tool.
• Verification: confirm assumptions (format, encoding, units, or environment) before changing anything.
• Consistency: apply one approach end-to-end so results are repeatable and easy to debug.

Common pitfalls

• Mistake: skipping validation and trusting the first output you see from Verify the output (fast, effective).
• Mistake: mixing formats or layers (for example, decoding the wrong field or using the wrong unit).

Quick checklist

1. Identify the exact input format and whether it is nested or transformed multiple times.
2. Apply the minimal transformation needed to make it readable.
3. Validate the result (structure, encoding, and expected markers).
4. If the result still looks encoded, repeat step-by-step and stop as soon as it becomes clear.

Why are numbers wrong?

Small fonts, blur, and low contrast are the most common reasons. Numbers also lack language context, so OCR has fewer clues.

What should I do if the output still looks encoded?

Decode step-by-step. If you still see obvious markers (percent codes, escape sequences, or Base64-like text), the data is likely nested.

What is the safest way to avoid bugs?

Keep the original input, change one thing at a time, and validate after each step so you know exactly what fixed the issue.

Should I use the decoded value in production requests?

Usually no. Decode for inspection and debugging, but send the original encoded form unless your protocol explicitly expects decoded text.

Why does it work in one environment but not another?

Different environments often have different settings (time zones, keys, encoders, or parsing rules). Compare a known-good sample side-by-side.

• Reminder: verify inputs and outputs for "FAQ" with a known-good sample.
• Reminder: verify inputs and outputs for "FAQ" with a known-good sample.

• ISO 32000-2 (PDF 2.0) - PDF specification.
• Adobe PDF Reference - PDF reference docs.
• PDF/A overview (ISO 19005) - Archival PDF standard.
• Tesseract OCR - Open source OCR engine.
• Google Cloud Vision OCR - OCR API overview.
• W3C Web Content Accessibility Guidelines (WCAG) - Accessibility reference.
• NIST IR 8071 (OCR evaluation) - OCR testing study.
• ISO/IEC 19794-5 (image data) - Image data standard.
• ALTO XML - OCR output format.
• hOCR specification - OCR output format.

Key takeaways

• Definition: References clarifies what the input represents and what the output should mean.
• Why it matters: correct interpretation prevents downstream bugs and incorrect conclusions.
• Validation: confirm assumptions before changing formats, units, or encodings.
• Repeatability: use the same steps each time so results are consistent across environments.

Common pitfalls

• Mistake: skipping validation and trusting the first output you see in References.
• Mistake: mixing formats or layers (for example, decoding the wrong field or using the wrong unit).
• Mistake: losing the original input, making it impossible to reproduce the issue.

Quick checklist

1. Identify the exact input format and whether it is nested or transformed multiple times.
2. Apply the minimal transformation needed to make it readable.
3. Validate the result (structure, encoding, expected markers) before acting on it.
4. Stop as soon as the result is clear; avoid over-decoding or over-normalizing.