Matches in SemOpenAlex for { <https://semopenalex.org/work/W4317830805> ?p ?o ?g. }
- W4317830805 endingPage "92" @default.
- W4317830805 startingPage "75" @default.
- W4317830805 abstract "The handwritten text recognition problem is widely studied by the researchers of computer vision community due to its scope of improvement and applicability to daily lives. It is a sub-domain of pattern recognition. Due to advancement of computational power of computers since last few decades neural networks based systems heavily contributed towards providing the state-of-the-art handwritten text recognizers. In the same direction, we have taken two state-of-the art neural networks systems and merged the attention mechanism with it. The attention technique has been widely used in the domain of neural machine translations and automatic speech recognition and now is being implemented in text recognition domain. In this study, we are able to achieve 4.15% character error rate and 9.72% word error rate on IAM dataset, 7.07% character error rate and 16.14% word error rate on GW dataset after merging the attention and word beam search decoder with existing Flor et al. architecture. To analyse further, we have also used system similar to Shi et al. neural network system with greedy decoder and observed 23.27% improvement in character error rate from the base model." @default.
- W4317830805 created "2023-01-24" @default.
- W4317830805 creator A5005714809 @default.
- W4317830805 creator A5006167766 @default.
- W4317830805 creator A5010428256 @default.
- W4317830805 creator A5035406249 @default.
- W4317830805 date "2022-12-15" @default.
- W4317830805 modified "2023-09-25" @default.
- W4317830805 title "Lexicon and attention based handwritten text recognition system" @default.
- W4317830805 cites W1902237438 @default.
- W4317830805 cites W1967935888 @default.
- W4317830805 cites W1992152861 @default.
- W4317830805 cites W1993155451 @default.
- W4317830805 cites W2018970719 @default.
- W4317830805 cites W2021043164 @default.
- W4317830805 cites W2051654810 @default.
- W4317830805 cites W2053317383 @default.
- W4317830805 cites W2061994628 @default.
- W4317830805 cites W2084859622 @default.
- W4317830805 cites W2106986062 @default.
- W4317830805 cites W2107346887 @default.
- W4317830805 cites W2122585011 @default.
- W4317830805 cites W2127141656 @default.
- W4317830805 cites W2128060444 @default.
- W4317830805 cites W2141190215 @default.
- W4317830805 cites W2147393756 @default.
- W4317830805 cites W2152928267 @default.
- W4317830805 cites W2153873029 @default.
- W4317830805 cites W2162395775 @default.
- W4317830805 cites W2167042387 @default.
- W4317830805 cites W2194187530 @default.
- W4317830805 cites W2293634267 @default.
- W4317830805 cites W2481930807 @default.
- W4317830805 cites W2574462282 @default.
- W4317830805 cites W2618530766 @default.
- W4317830805 cites W2786974559 @default.
- W4317830805 cites W2787260200 @default.
- W4317830805 cites W2904970205 @default.
- W4317830805 cites W2906383034 @default.
- W4317830805 cites W2946921758 @default.
- W4317830805 cites W2962707697 @default.
- W4317830805 cites W2964081096 @default.
- W4317830805 cites W2966163367 @default.
- W4317830805 cites W2984329012 @default.
- W4317830805 cites W3003428515 @default.
- W4317830805 cites W3031548983 @default.
- W4317830805 cites W3034726763 @default.
- W4317830805 cites W3038203375 @default.
- W4317830805 cites W3110453829 @default.
- W4317830805 cites W3160260209 @default.
- W4317830805 cites W4294306413 @default.
- W4317830805 cites W4298300677 @default.
- W4317830805 doi "https://doi.org/10.22630/mgv.2022.31.1.4" @default.
- W4317830805 hasPublicationYear "2022" @default.
- W4317830805 type Work @default.
- W4317830805 citedByCount "0" @default.
- W4317830805 crossrefType "journal-article" @default.
- W4317830805 hasAuthorship W4317830805A5005714809 @default.
- W4317830805 hasAuthorship W4317830805A5006167766 @default.
- W4317830805 hasAuthorship W4317830805A5010428256 @default.
- W4317830805 hasAuthorship W4317830805A5035406249 @default.
- W4317830805 hasBestOaLocation W43178308051 @default.
- W4317830805 hasConcept C115961682 @default.
- W4317830805 hasConcept C134306372 @default.
- W4317830805 hasConcept C138885662 @default.
- W4317830805 hasConcept C153180895 @default.
- W4317830805 hasConcept C154945302 @default.
- W4317830805 hasConcept C17649283 @default.
- W4317830805 hasConcept C204321447 @default.
- W4317830805 hasConcept C2524010 @default.
- W4317830805 hasConcept C2778121359 @default.
- W4317830805 hasConcept C2780861071 @default.
- W4317830805 hasConcept C28490314 @default.
- W4317830805 hasConcept C2987247673 @default.
- W4317830805 hasConcept C33923547 @default.
- W4317830805 hasConcept C36503486 @default.
- W4317830805 hasConcept C40969351 @default.
- W4317830805 hasConcept C41008148 @default.
- W4317830805 hasConcept C41895202 @default.
- W4317830805 hasConcept C44868376 @default.
- W4317830805 hasConcept C50644808 @default.
- W4317830805 hasConcept C546480517 @default.
- W4317830805 hasConcept C90805587 @default.
- W4317830805 hasConceptScore W4317830805C115961682 @default.
- W4317830805 hasConceptScore W4317830805C134306372 @default.
- W4317830805 hasConceptScore W4317830805C138885662 @default.
- W4317830805 hasConceptScore W4317830805C153180895 @default.
- W4317830805 hasConceptScore W4317830805C154945302 @default.
- W4317830805 hasConceptScore W4317830805C17649283 @default.
- W4317830805 hasConceptScore W4317830805C204321447 @default.
- W4317830805 hasConceptScore W4317830805C2524010 @default.
- W4317830805 hasConceptScore W4317830805C2778121359 @default.
- W4317830805 hasConceptScore W4317830805C2780861071 @default.
- W4317830805 hasConceptScore W4317830805C28490314 @default.
- W4317830805 hasConceptScore W4317830805C2987247673 @default.
- W4317830805 hasConceptScore W4317830805C33923547 @default.
- W4317830805 hasConceptScore W4317830805C36503486 @default.
- W4317830805 hasConceptScore W4317830805C40969351 @default.