We were discussing Signature verification methods. We reviewed the stages involved with Signature verification yesterday. We also enumerated the feature extraction techniques. Then we compared online and offline verification techniques.

One of the reasons offline image processing is preferred is that good image processing algorithms are often computationally expensive and require more time than say network roundtrip for packets. This makes it costly to include as an interactive web page analysis widget.  Time taken to execute image processing algorithms have taken even eight seconds. That is why image processing finds it difficult to keep up with the frame rate of a video. However, significant advances have been made that improve processing for streaming of images to be processed. For example, Active contour model can help track movement of object in images for a frame rate that matches the rate used for video. Signatures are considered a lot simpler to work with in image processing. They are generally small sized, binary color and easy to capture and process.  As long as the image processing can tell apart a real signature from forged specimens, an image processor can work in the backend for a signature pad widget in the front-end.

We talked about the acceptance criteria for an image processing technique that is largely measured by the precision and recall. By training the processor on a signature dataset, these processors become highly effective in determining even forged from real specimens. Today we will take a closer look at how this verification is done. Since we read how classifiers work in text processing to convert the document into a vector space model and then classify the document based on euclidean distance between feature vectors, the signature verification should also be familiar. The features extracted from the image as described in the previous posts is transformed into the vector space and then compared with the master. If the euclidean distance is within tolerance threshold, the signature is accepted. Since the image processor is already trained and tested on a variety of images and measured with precision and recall, it is reliable to convert the given specimen into a representative feature vector. This concludes the signature verification technique.


#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 efficient. With this theorem, we already know the number of combinations that can exist with duplicates and therefore do not enumerate them but directly count them towards the goal such as the price of the accessories shopped. The theorem mentioned used a binomial coefficient.