Hybrid HMM/BLSTM system for multi-script keyword spotting in printed and handwritten documents with identification stage

In this paper, we propose a novel script-independent approach for word spotting in printed and handwritten multi-script documents. Since each writing type and script need to be processed using a specific spotting engine, the proposed system proceeds on two stages. The script identification is a preliminary stage that aims at recognizing on one level the writing type and the script of the input image document. Second, a specific word spotting method is used to spot query words in a large collection of documents. The proposed spotting system is based on deep bidirectional long short-term memory neural network and hidden Markov model (HMM) hybrid architecture. It takes advantage of DNN's strong representation learning power and HMM's sequential modeling ability. The global system has been evaluated on a mixed corpus of public databases such as KHATT, PKHATT for Arabic script and RIMES for Latin script. The experimental results on script identification and keyword spotting confirm the effectiveness of the proposed approach.