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Facing high error rates and slow recognition speed for full text transcription of unconstrained handwriting images, keyword spotting is a promising alternative to locate specific search terms within scanned document images. We have previously proposed a learning-based method for keyword spotting using character hidden
Two methods are presented to improve word confidence scores for Line-Level Query-by-String Lexicon-Free Keyword Spotting (KWS) in handwritten text images. The first one approaches true relevance probabilities by means of computations directly carried out on character lattices obtained from the lines images considered
The recurrent neural network language model (RNNLM) is a discriminative, non-Markovian model that can capture long-span word history in natural language. It has been proved to be successful in automatic speech recognition and machine translation. In this work, we applied RNNLM to the n-best rescoring stage of the state-of-the-art BBN Byblos OCR (optical character recognition) system for handwriting...
-by-example), which can retrieve morphologically similar words that have matching sub-words. Further, to enable query-by-keyword, we build an automated scheme to generate labeled exemplars for characters and character n-grams, from unconstrained handwritten documents. We pose this problem as one of weakly-supervised
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