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The classifier's confidence score can be used to determine the reliability of the model's prediction. A high confidence score indicates that the model is confident in its prediction, while a low confidence score suggests that the model is unsure about its prediction.

A confidence score is assigned and output by the OCR engine for each field answer. The field-level confidence score uses the "raw" OCR character- and word-level scores and synthesizes them with other available information to arrive at a final score.

A confidence score is calculated as an evaluation standard. This confidence score shows the probability of the image being detected correctly by the algorithm and is given as a percentage. The scores are taken on the mean average precision at different IoU (Intersection over Union) thresholds.

OCR Accuracy is defined as the process of comparing the output of OCR with the original version of the same (ground truth) text. Let's say a document had 100 characters (ground truth). If the OCR output text correctly identified 99 of them, the character level OCR accuracy is 99%.

Confidence Score / Confidence Level The Confidence Score, or Classification Threshhold, indicates how sure the NLP service / machine learning model is that the respective intent was correctly assigned. The score can have a value between 0 and 1 , depending on how the neural networks work.

A confidence score is calculated as an evaluation standard. This confidence score shows the probability of the image being detected correctly by the algorithm and is given as a percentage. The scores are taken on the mean average precision at different IoU (Intersection over Union) thresholds.

The confidence score is a number between 0 and 100. A score of 100 is likely an exact match, while a score of 0 means, that no matching answer was found. The higher the score- the greater the confidence in the answer. For a given query, there could be multiple answers returned.