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From last decade, machine learning (ML) techniques have been used for Urdu text processing. Due to lack of language resources, potential of deep learning (DL) models have not been exploited yet for Urdu text document classification. A text document has more noise, redundant information, and large vocabulary than short text like tweets. This study is the systematic comparison of four well-known DL models. We also compare DL models with four ML models. We also explore the various text preprocessing techniques. Experimental results show that CNN outperforms the others. Further, single-layer architecture of LSTM and BiLSTM performs better than multiple-layers architecture.

Exploring deep learning approaches for Urdu text classification in product manufacturing