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

翻译：每个场景文本识别（STR）任务都由文本定位和文本识别作为主要子任务组成。然而，在固定摄像头位置的实际应用中，例如设备监控读数、基于图像的数据录入以及印刷文档数据提取，目标数据通常是规则场景文本。因此，在这些任务中，使用通用的大型模型相比定制的高效模型，在模型可部署性、数据隐私和模型可靠性方面存在显著劣势。为此，本文介绍了高度专门化于任务本身的模型开发所涉及的基本概念、理论、实现及实验结果，旨在不仅达到最先进的性能，还具备最小的模型权重、更短的推理时间和高的模型可靠性。我们提出了一种新颖的深度学习架构（GeoTRNet），该架构仅利用存在的几何特征进行训练，以识别规则场景图像中的数字，模仿人类对文本识别的感知方式。代码已公开发布于 https://github.com/ACRA-FL/GeoTRNet。