We were discussing Signature verification methods. We reviewed the stages involved with Signature verification yesterday. We also enumerated the feature extraction techniques. Now let us proceed to comparing online and offline verification techniques.
An offline signature processing algorithm requires all the information before the algorithm starts. This gives us opportunity to perform all the pre-processing required to normalize the dataset for the algorithm to work effectively. The online algorithm might work on the data while the data is being made available. The processor may reside as close to the sensing device as necessary to make this happened. In the offline case, the processor may even be in a backend system of the office. The image recognition for handwritten signatures has traditionally been offline processing. Even as such, it has been more optical based and not magnetic based. With the list of features compared between the two in the online and offline systems, we see the difference in what can be used online. Online techniques have been said to be more accurate because the system is getting the data as the user feeds it. Offline comparision can eliminate the quirks of the device on which the data is being submitted and can work effectively across a variety of devices and vendors. Online processing helps standalone processors that can be mobile and may have its own local database.
The acceptance criteria for an image processing technique is largely measured by the precision and recall. Precision in this case is the ratio that explains number of selected items that are relevant. It is the ration of the true positives to all that were selected by the image processor for this image. A true positive is one that improves the feature matching. A false positive doesn’t but shows up in match threshold. Recall on the other hand is a metric that determines how many relevant items are selected. It is the ratio of the true positives to all those that would have improved the feature matching from the global set feature matches including ones that the processor did not select. Together precision and recall yields a different metric called F-score which gives the effectiveness of retrieval with respect to a given image. By training the processor on a signature dataset, these processors become highly effective in determining even forged from real specimens.
#codingexercise
We were discussing combinations with duplicates and that too in a greedy manner. instead of enumerating combinations to the whole length, we can leverage stars and bars theorem to be more effecient.
An offline signature processing algorithm requires all the information before the algorithm starts. This gives us opportunity to perform all the pre-processing required to normalize the dataset for the algorithm to work effectively. The online algorithm might work on the data while the data is being made available. The processor may reside as close to the sensing device as necessary to make this happened. In the offline case, the processor may even be in a backend system of the office. The image recognition for handwritten signatures has traditionally been offline processing. Even as such, it has been more optical based and not magnetic based. With the list of features compared between the two in the online and offline systems, we see the difference in what can be used online. Online techniques have been said to be more accurate because the system is getting the data as the user feeds it. Offline comparision can eliminate the quirks of the device on which the data is being submitted and can work effectively across a variety of devices and vendors. Online processing helps standalone processors that can be mobile and may have its own local database.
The acceptance criteria for an image processing technique is largely measured by the precision and recall. Precision in this case is the ratio that explains number of selected items that are relevant. It is the ration of the true positives to all that were selected by the image processor for this image. A true positive is one that improves the feature matching. A false positive doesn’t but shows up in match threshold. Recall on the other hand is a metric that determines how many relevant items are selected. It is the ratio of the true positives to all those that would have improved the feature matching from the global set feature matches including ones that the processor did not select. Together precision and recall yields a different metric called F-score which gives the effectiveness of retrieval with respect to a given image. By training the processor on a signature dataset, these processors become highly effective in determining even forged from real specimens.
#codingexercise
We were discussing combinations with duplicates and that too in a greedy manner. instead of enumerating combinations to the whole length, we can leverage stars and bars theorem to be more effecient.
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