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Deep learning-based recognition system for pashto handwritten text: benchmark on PHTI.

Ibrar Hussain^1,2, Riaz Ahmad¹, Khalil Ullah³

¹Department of Computer Science, Shaheed Benazir Bhutto University, Sheringel, Dir, Pakistan.

Peerj. Computer Science

|April 25, 2024

Summary

This summary is machine-generated.

This study presents the first baseline system for Pashto handwritten text recognition using multi-dimensional long short-term memory (MD-LSTM) networks. The system achieved a 20.77% Character Error Rate (CER), offering insights for the digital transition of the Pashto language.

Keywords:

Deep learning Natural language processing Optical character recognition Pashto handwritten text imagebase

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Area of Science:

Natural Language Processing
Computer Vision
Machine Learning

Background:

The Pashto language lacks a baseline system for handwritten text recognition.
Digital transition of languages requires robust recognition systems.

Purpose of the Study:

To introduce the first baseline recognition system for Pashto handwritten text.
To evaluate the performance of a multi-dimensional long short-term memory (MD-LSTM) network on the Pashto Handwritten Text Imagebase (PHTI) dataset.

Main Methods:

Pre-processing of the PHTI dataset to remove unwanted characters.
Development of a recognition system utilizing multi-dimensional long short-term memory (MD-LSTM) networks.
Empirical analysis to optimize MD-LSTM parameters and comparative experiments against state-of-the-art models.

Main Results:

The proposed MD-LSTM system achieved a baseline Character Error Rate (CER) of 20.77% on the PHTI test set.
Novelty in hidden layer size (10, 20, 80) and Tanh layer size (20, 40) was explored.
Top 20 confusions were analyzed to understand model limitations.

Conclusions:

The developed system establishes a baseline for Pashto handwritten text recognition.
Results indicate challenges and future directions for Pashto language's digital transition.
Further research is needed to improve recognition accuracy and address language-specific complexities.