  Every Scene Text Recognition (STR) task consists of text localization \& text
recognition as the prominent sub-tasks. However, in real-world applications
with fixed camera positions such as equipment monitor reading, image-based data
entry, and printed document data extraction, the underlying data tends to be
regular scene text. Hence, in these tasks, the use of generic, bulky models
comes up with significant disadvantages compared to customized, efficient
models in terms of model deployability, data privacy \& model reliability.
Therefore, this paper introduces the underlying concepts, theory,
implementation, and experiment results to develop models, which are highly
specialized for the task itself, to achieve not only the SOTA performance but
also to have minimal model weights, shorter inference time, and high model
reliability. We introduce a novel deep learning architecture (GeoTRNet),
trained to identify digits in a regular scene image, only using the geometrical
features present, mimicking human perception over text recognition. The code is
publicly available at https://github.com/ACRA-FL/GeoTRNet
