It is proposed to collect ca. 50,000 markes, initials and signatures (collectively known as signoff data) for subsequent pattern recognition and machine learning work.
Should we collect more data?
Do we require different amounts of data for the three proposed categories markes, initials and signatures due to different visual characteristics of these categories?
What benefits do we anticipate from greater volumes of data? For example, doubling or tripling the volumes?
What are the collection and processing costs of acquiring and working with greater volumes of data?
Does the quantity of data we require vary accoring to specific machine learning techniques we may use?
these are good questions but hard to tackle without a closer knowledge of the data. What I would say is that:
more data is better, if it includes something different and learnable from the original dataset and not much noise;
I would intuitively say that probably signatures are intrinsically more complex than initials (more letters, for example), thus I would go with more data there. No idea about markes;
cannot say, probably diminishing anyway;
depends how you plan to acquire it, usually the processing cost do not change, the acquisition costs grow slightly sub-linearly with the amount of data;
yes, but again it is difficult to say, and anyway it might be less of a problem than redundant, noisy or variably represented data.
It is proposed to collect ca. 50,000 markes, initials and signatures (collectively known as signoff data) for subsequent pattern recognition and machine learning work.