What algorithm should be used to auto-correct recognition errors?

Question

Text recognising

When recognizing container numbers according to the ISO 6346 standard
by smart cameras (Android Things, Java, ml-kit ), typical errors occur (A = 4, 0 = O, 22G1 = 2261).
Previously, such cases were corrected by the operator, now there is a need to minimize such errors.
What is the best approach to use here?
It suggests sequential replacement of typical erroneous characters with their correspondences and rechecking the checksum until the sum matches (limit to one or two replacements), but it is possible that the value of the sum itself is not recognized correctly and then we can adjust the container number itself to the wrong number.
What other "pitfalls" can there be in this approach?

Answer 1

Modify the shooting and recognition code so that the shooting cycle is performed until the license plate is successfully recognized. Don't forget the limit on the number of recognition cycles.
For convenience, instead of 1 picture, take several pictures with an interval of 100-200 ms.