  Long-term OCR services aim to provide high-quality output to their users at
competitive costs. It is essential to upgrade the models because of the complex
data loaded by the users. The service providers encourage the users who provide
data where the OCR model fails by rewarding them based on data complexity,
readability, and available budget. Hitherto, the OCR works include preparing
the models on standard datasets without considering the end-users. We propose a
strategy of consistently upgrading an existing Handwritten Hindi OCR model
three times on the dataset of 15 users. We fix the budget of 4 users for each
iteration. For the first iteration, the model directly trains on the dataset
from the first four users. For the rest iteration, all remaining users write a
page each, which service providers later analyze to select the 4 (new) best
users based on the quality of predictions on the human-readable words. Selected
users write 23 more pages for upgrading the model. We upgrade the model with
Curriculum Learning (CL) on the data available in the current iteration and
compare the subset from previous iterations. The upgraded model is tested on a
held-out set of one page each from all 23 users. We provide insights into our
investigations on the effect of CL, user selection, and especially the data
from unseen writing styles. Our work can be used for long-term OCR services in
crowd-sourcing scenarios for the service providers and end users.
