Handwriting Recognition for Cursive Scripts: A Case Study on Urdu Text

Abstract

Recognition of cursive handwritten text is a complex problem due challenges like context-sensitive character shapes, non-uniform inter and intra word spacings, complex positioning of dots and diacritics and very low inter-class variation among certain classes. This research study presents an effective technique for recognition of cursive handwritten text using Urdu as a case study (though findings can be generalized to other cursive scripts as well). We present an analytical approach based on implicit character segmentation where CNNs are employed as feature extractors and LSTM network is used as a classifier. The proposed technique is validated on UNHD and UHTI (custom generated data set) and reported promising recognition results.