We were discussing Signature verification methods. We reviewed the stages involved with Signature verification. Then we also enumerated the feature extraction techniques. After that, we compared online and offline verification techniques. Yesterday we discussed the limitations of image processing and the adaptations for video processing. Today we continue with the discussion on relevant improvements for signature processing.
Unlike earlier, when image processing was confined to research labs and industrial automations, there are now software libraries, packages and applications available. Moreover, services for image processing are no longer restricted in compute and storage because they can now be hosted in the cloud. Many cloud providers now also provide libraries for image processing. For example, Microsoft and Google both provide image processing libraries. Perhaps Clarifai has a dedicated offering in this discipline.
The reason I bring out these companies is that this area of study also benefits from a multidisciplinary approach. For example, Microsoft's machine learning algorithms and R-package covered earlier in this post may also be relevant to image processing after images are transformed to a vector space model. Similarly Google's application of word2vec to perform word embeddings may provide insight into object embedding in images. Clarifai provides an api library and makes image processing just as commercial to develop as it is fun to experiment in Matlab.
Signature processing benefits incredibly with the right choice of algorithms. We don't need to perform edge segmentation since the data may already be smoothed and made clear in the preprocessing step. Gaussian smoothing helps in this regard because it adjusts the value of the current pixel based on the values of the surrounding pixels. After the pre-processing, the offline verification of signature becomes straightforward as we rely on a choice of algorithms from the previously covered list to perform this verification. If we have the luxury of performing these comparisions simultaneously, we can then perform a collaborative filtering of the given sample as a valid or invalid in a serverless computing paradigm. This is a break from the previously mentioned software for signature verification.
Technically this does not seem impossible but as we fine tune the algorithm and user acceptance may determine the success of such a venture. Signatures unlike passwords are handwritings. They are susceptible to the mood and circumstance. Since the input may change each time, the verification has to give such latitude to the user.
Unlike earlier, when image processing was confined to research labs and industrial automations, there are now software libraries, packages and applications available. Moreover, services for image processing are no longer restricted in compute and storage because they can now be hosted in the cloud. Many cloud providers now also provide libraries for image processing. For example, Microsoft and Google both provide image processing libraries. Perhaps Clarifai has a dedicated offering in this discipline.
The reason I bring out these companies is that this area of study also benefits from a multidisciplinary approach. For example, Microsoft's machine learning algorithms and R-package covered earlier in this post may also be relevant to image processing after images are transformed to a vector space model. Similarly Google's application of word2vec to perform word embeddings may provide insight into object embedding in images. Clarifai provides an api library and makes image processing just as commercial to develop as it is fun to experiment in Matlab.
Signature processing benefits incredibly with the right choice of algorithms. We don't need to perform edge segmentation since the data may already be smoothed and made clear in the preprocessing step. Gaussian smoothing helps in this regard because it adjusts the value of the current pixel based on the values of the surrounding pixels. After the pre-processing, the offline verification of signature becomes straightforward as we rely on a choice of algorithms from the previously covered list to perform this verification. If we have the luxury of performing these comparisions simultaneously, we can then perform a collaborative filtering of the given sample as a valid or invalid in a serverless computing paradigm. This is a break from the previously mentioned software for signature verification.
Technically this does not seem impossible but as we fine tune the algorithm and user acceptance may determine the success of such a venture. Signatures unlike passwords are handwritings. They are susceptible to the mood and circumstance. Since the input may change each time, the verification has to give such latitude to the user.
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