A keyword spotting approach using blurred shape model-based descriptors
Proceedings of the 2011 Workshop on Historical Document Imaging and Processing
A synthesised word approach to word retrieval in handwritten documents
Pattern Recognition
Handwriting recognition in historical documents using very large vocabularies
Proceedings of the 2nd International Workshop on Historical Document Imaging and Processing
Contextual word spotting in historical manuscripts using Markov logic networks
Proceedings of the 2nd International Workshop on Historical Document Imaging and Processing
Learning-based word spotting system for Arabic handwritten documents
Pattern Recognition
Statistical script independent word spotting in offline handwritten documents
Pattern Recognition
Keyword spotting in unconstrained handwritten Chinese documents using contextual word model
Image and Vision Computing
| Hi-index | 0.14 |
Keyword spotting refers to the process of retrieving all instances of a given keyword from a document. In the present paper, a novel keyword spotting method for handwritten documents is described. It is derived from a neural network-based system for unconstrained handwriting recognition. As such it performs template-free spotting, i.e., it is not necessary for a keyword to appear in the training set. The keyword spotting is done using a modification of the CTC Token Passing algorithm in conjunction with a recurrent neural network. We demonstrate that the proposed systems outperform not only a classical dynamic time warping-based approach but also a modern keyword spotting system, based on hidden Markov models. Furthermore, we analyze the performance of the underlying neural networks when using them in a recognition task followed by keyword spotting on the produced transcription. We point out the advantages of keyword spotting when compared to classic text line recognition.