Description

Cursive Handwritten Text Recognition using LSTM -Deep Learning Projects- Matlab

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 deep learning presents an effective technique for recognition of cursive handwritten text using english 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 convolutional neural networks (CNNs) are employed as feature extractors while classification is carried out using a bi-directional Long-Short-Term Memory (LSTM) network.

The proposed technique is validated on a dataset of 50 unique handwritten text lines reporting promising character recognition rates.