Printed text recognition using BLSTM and MDLSTM for Indian languages
Vishal Chavan, Abhijit Malage, Kapil Mehrotra, Manish Kumar Gupta · 2017
In this paper, we evaluated the recognition performance of BLSTM (Bidirectional LSTM) and MDLSTM (two-dimensional LSTM) neural network architecture on printed documents. We also compare the performance of 2 architectures with tesseract on same test bed. We demonstrate our experimentation on 7 Indian languages i.e. Hindi, Marathi, Tamil, Kannada, Malayalam, Bangla and Gurumukhi. The input to both the architecture will be segmented lines. The data-set used contains approximate 5000 pages for each language which then divided into train, validation and test set. The Histogram of Gradients are extracted at line level to feed into the BLSTM network. Whereas MDLSTM processes 2D image (raw pixels) of each line. The level and number of hidden layers in both the architectures are empirically selected and kept same for all the languages. The output CTC layer will contain the number of unicode present in the evaluated languages and one blank label. The input layer was fully connected to hidden layers, and these were fully connected to themselves and to the output layer. The validated result shows MDLSTM outperforms both BLSTM and tesseract for all the languages included in our experimentation.