Keyword Recognition by Improving Recurrent Neural Network using Character Model |
Author(s): |
| Geetanjali Bhagwani , L.J. INSTITUTE OF ENGINEERING AND TECHNOLOGY; Ompriya Kale, L.J. INSTITUTE OF ENGINEERING AND TECHNOLOGY |
Keywords: |
| Handwritten recognition, keyword spotting, bidirectional long short-term memory, recurrent neural network, CTC token passing algorithm, Character model |
Abstract |
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Keyword spotting refers to the process of retrieving all instances of a given keyword from these documents. In the present paper, a keyword spotting method for handwritten documents is obtained using different various systems for offline handwriting recognition. A new technique is used for robust keyword spotting that uses bidirectional Long Short-Term Memory (BLSTM) recurrent neural nets and CTC Token Passing Algorithm to incorporate contextual information in documents. The document analysis is done by demonstrating tri-gram character model to significantly improve the spotting performance on IAM offline database. |
Other Details |
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Paper ID: IJSRDV3I40681 Published in: Volume : 3, Issue : 4 Publication Date: 01/07/2015 Page(s): 1306-1309 |
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