Recognize and Validate Text
The reader extracts a postal code from a blurred form.
Read the full lesson and open its transcript below. A narrated video will be added after production.
What you will learn
The reader extracts a postal code from a blurred form.
A practical question
The crop clearly contains a permit code, yet the recognizer reads a zero as the letter O. Recognition converts visible strokes into characters, but similar shapes and glare can produce several plausible strings.
The useful idea
Use a known format to validate the result without silently changing uncertain characters. If permits have a defined length or checksum, that rule can flag an impossible reading. It doesn't prove that every format-valid result is correct. Keep the image crop and confidence information for review.
Work through the example
Compare the raw reading, its validated interpretation, and the original permit. Try a damaged sample where two codes fit the pattern. Ask for human review rather than guessing a character that controls access. An optical reader may produce a plausible-looking code that differs by one character from the image. Check the expected format, such as length and allowed symbols, but do not silently replace uncertain characters just to pass validation. Show the original crop and the recognition confidence to a reviewer. A valid format does not prove the code belongs to the correct document or field. Preserve uncertainty until someone or another trusted source can resolve it. Now check the idea on a concrete example: Validate a five-digit code against its expected length while retaining the original crop for review. A possible trap is worth naming: A valid-looking string can still be the wrong name or address. A one-character error in a permit code may pass unnoticed when the string still looks reasonable. Validate against the known format, show the ambiguous crop, and preserve the original recognition output for review rather than silently correcting it. A plausible replacement character is still an inference; someone must be able to compare it with the source image.
Limits and next step
Next, we'll connect recognized pieces of text to named fields on a form instead of treating the page as one long string.
Worked example
Validate a five-digit code against its expected length while retaining the original crop for review.
Quick check: test your understanding
Try it yourself: Consider this case: Validate a five-digit code against its expected length while retaining the original crop for review. Explain how you would check the result. In your answer, address this limitation: A valid-looking string can still be the wrong name or address.
Check the reasoning
OCR predicts characters; domain checks can flag unlikely formats or missing fields. A valid-looking string can still be the wrong name or address.
What is the main idea behind recognize and validate text?
OCR predicts characters; domain checks can flag unlikely formats or missing fields.
How is it used in the example?
Validate a five-digit code against its expected length while retaining the original crop for review.
What limitation should be checked?
A valid-looking string can still be the wrong name or address.
Further reading
Programming Computer Vision with Python — Jan Erik Solem.
Original ToolsInVision teaching examples. Editorial approach · Suggest a correction
Accessible referenceLesson 66 transcript305 words · Narration ready for the upcoming videoOpen transcript
The crop clearly contains a permit code, yet the recognizer reads a zero as the letter O. Recognition converts visible strokes into characters, but similar shapes and glare can produce several plausible strings.
Use a known format to validate the result without silently changing uncertain characters. If permits have a defined length or checksum, that rule can flag an impossible reading. It doesn't prove that every format-valid result is correct. Keep the image crop and confidence information for review.
Compare the raw reading, its validated interpretation, and the original permit. Try a damaged sample where two codes fit the pattern. Ask for human review rather than guessing a character that controls access. An optical reader may produce a plausible-looking code that differs by one character from the image. Check the expected format, such as length and allowed symbols, but do not silently replace uncertain characters just to pass validation. Show the original crop and the recognition confidence to a reviewer. A valid format does not prove the code belongs to the correct document or field. Preserve uncertainty until someone or another trusted source can resolve it. Now check the idea on a concrete example: Validate a five-digit code against its expected length while retaining the original crop for review. A possible trap is worth naming: A valid-looking string can still be the wrong name or address. A one-character error in a permit code may pass unnoticed when the string still looks reasonable. Validate against the known format, show the ambiguous crop, and preserve the original recognition output for review rather than silently correcting it. A plausible replacement character is still an inference; someone must be able to compare it with the source image.
Next, we'll connect recognized pieces of text to named fields on a form instead of treating the page as one long string.