Abstract
Pashto is the native language of Afghanistan and one of Pakistan's most essential and regional languages. The Pashto language has a vast number of native speakers who live in various parts of the world. The handwritten Pashto textual trajectories are hard to recognize and detect due to the cursive style and handwriting variation. The transformation behaviour, i.e., scaling, rotation, and shifting of handwritten text, are the prominent but challenging factors. A lightweight deep learning-based model construction for low and medium-resource devices in a less-constrained environment is challenging. This paper provides a practical, light deep learning-based model for predicting handwritten Pashto words. A massive Pashto-transformed invariant inverted handwritten text dataset is prepared with the help of the Pashtun community. A lightweight MobileNetV2 has been highly tuned for Pashto handwritten text classification, extracting images' features (MoI). We inverted the dataset to make the model more accurate and restrict it to fifteen epochs. Extensive experiments have been conducted to validate the suggested model's performance. The proposed transformed invariant lightweight Pashto deep learning (TILPDeep) technique achieves a training accuracy of 0.9839 and a validation accuracy of 0.9405 for transformed invariant Pashto handwritten inverted text using recognition matrices.