Overview
To enhance the usefulness of response confidence, Ocrolus maps it to different statistically relevant estimations that can assist client-side automation logic.
Model Name: FormType
Details about the type of the submitted form.
Properties
confidence
confidence- Type
-
Double (
double) - Required
- true
- Min. Value
- 0
- Max. Value
- 1
Our model's confidence in the document's classification, ranged between 0 and 1. Higher values indicate better predictions.
name
name- Type
- String
- Pattern
/^[A-Z0-9_-]+$/- Required
- true
- Min. Length
- 1
The form code of the classified document.
precision
precision- Type Double
- double
- Required
- true
- Min. Value
- 0
- Max. Value
- 1
An estimation to statistically interpret the confidence number output by the model and evaluation of the likelihood of the provided value to be correct. This estimation can be used by the client to filter responses, potentially increasing the system's perceived precision. However, this approach may result in discarding some suggestions.
For example, if the desired error rate is less than 5%, a threshold of 0.95 can be set on the precision, and any responses falling below this threshold can be discarded.
In general, it is possible to trade off precision for recall. Relaxing the precision requirement can lead to an increase in recall.
recall
recall- Type Double
- double
- Required
- true
- Min. Value
- 0
- Max. Value
- 1
An estimation to statistically interpret the confidence number generated by the model in terms of the proportion of responses that are as precise or better than the current response. This information can be helpful in filtering for the most accurate responses.
For example, to accept 80% of responses and subject the remaining 20% to further review, we can accept any response with a recall score below 0.80 and review those above the threshold.
precision_tags
precision_tags- Type
- List of strings
- Required
- true
Ocrolus adds tags to responses when a static threshold is not applicable or would vary too much across different fields.
-
Optimal_f1_score: This tag indicates that the precision is higher than what one would choose to optimize for both precision and recall. It's also known as the F1 score. This tag can be useful for filtering output if there is no specific error rate goal.
-
Targeted_95_precision_threshold: This is the deprecated tag and has been replaced by the precision tag. This tag will be present when the precision is higher than 0.95.
-
Targeted_98_precision_threshold: This is the deprecated tag and has been replaced by the precision tag. This tag will be present when the precision is higher than 0.98.