Handwritten character recognition (HCR) is still regarded as a difficult learning problem in pattern recognition, even after being studied in-depth for a few decades. Research on script independent models is also scarce. This can be linked to several things, especially character structure similarities, variances in handwriting styles, noisy datasets, script diversity, the conventional research's emphasis on manual feature extraction techniques, and the lack of publicly available datasets and code repositories to replicate the findings. However, deep learning offers from top to bottom learning together with has achieved great success in various pattern recognition domains, such as hand gesture recognition (HCR). Deep learning methods, on the other hand, are computationally costly, require a lot of data to train, and are limited to scripts. We have developed a novel generic Deep Progressively Learning Model architecture for script independent handwritten character recognition, known as HCR- DPLM, to overcome the limitations. The foundation of HCR-DPLM is a unique transfer learning strategy for HCR that makes use of a pre-trained network's feature extraction layers in part. HCR DPLM offers better performance and better generalizations, faster and computationally efficient training, and the ability to work with small datasets because of Transferring Knowledge and Imagery augmentation. In this paper, we use a pixel level classifier to extract the character pixels and eliminate noise from handwritten character images. © 2025 Elsevier B.V., All rights reserved.