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:::; OCR Text Evaluation : Evaluation of OCR Output will be based on a comparison to Gold Hand-Typed outputs, using confusion matrix like criteria for evaluating word presence, word correctness, and avoiding non-text garbage regions. We will attempt to avoid penalizing for attempts at text recognition in barcode and handwritten regions. | :::; OCR Text Evaluation : Evaluation of OCR Output will be based on a comparison to Gold Hand-Typed outputs, using confusion matrix like criteria for evaluating word presence, word correctness, and avoiding non-text garbage regions. We will attempt to avoid penalizing for attempts at text recognition in barcode and handwritten regions. | ||
:::; Parsed Field Evaluation : Evaluation of the effectiveness of parsing will be calculated based on a confusion matrix. Rows are named with each of the possible element names for parts of a label. Columns are also these same names. Counts along the diagonal represent the number of items that were tagged correctly. For example, a count that is correctly labeled as a county will add one to the diagonal. If a county is incorrectly marked as a stateProvince, a 1 is added to the “county” row under the stateProvince column. This format therefore provides a count of correct classifications and count of false positives and false negatives. We will calculate, precision, recall, f-score and potentially others. | :::; Parsed Field Evaluation : Evaluation of the effectiveness of parsing will be calculated based on a confusion matrix. Rows are named with each of the possible element names for parts of a label. Columns are also these same names. Counts along the diagonal represent the number of items that were tagged correctly. For example, a count that is correctly labeled as a county will add one to the diagonal. If a county is incorrectly marked as a stateProvince, a 1 is added to the “county” row under the stateProvince column. This format therefore provides a count of correct classifications and count of false positives and false negatives. We will calculate, precision, recall, f-score and potentially others. | ||
== Back to the [[2013 AOCR Hackathon Wiki| Hackathon Wiki]] == | |||
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