LEADER 05821nam 2200721Ia 450 001 9910465481503321 005 20200520144314.0 010 $a1-283-59373-4 010 $a9786613906182 010 $a981-4390-34-8 035 $a(CKB)2560000000093359 035 $a(EBL)1019616 035 $a(OCoLC)811504361 035 $a(SSID)ssj0000703140 035 $a(PQKBManifestationID)12278738 035 $a(PQKBTitleCode)TC0000703140 035 $a(PQKBWorkID)10689665 035 $a(PQKB)10204807 035 $a(MiAaPQ)EBC1019616 035 $a(WSP)00002777 035 $a(Au-PeEL)EBL1019616 035 $a(CaPaEBR)ebr10596916 035 $a(CaONFJC)MIL390618 035 $a(OCoLC)810933234 035 $a(EXLCZ)992560000000093359 100 $a20120111d2012 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aMultimodal Interactive handwritten text transcription$b[electronic resource] /$fVero?nica Romero, Alejandra He?ctor Toselli, Enrique Vidal 210 $aSingapore ;$aHackensack, NJ $cWorld Scientific Pub$dc2012 215 $a1 online resource (180 p.) 225 0$aSeries in machine perception and artificial intelligence ;$vv. 80 300 $aDescription based upon print version of record. 311 $a981-4390-33-X 320 $aIncludes bibliographical reference (p. 155-164) and index. 327 $aContents; Preface; 1. Preliminaries; 1.1 Introduction; 1.2 State of the Art; 1.2.1 Optical Character Recognition; 1.2.2 Handwritten Text Recognition; 1.3 Formal Background; 1.3.1 Hidden Markov Models; Continuous HMM; Basic algorithms for HMMs; The Decoding Problem and the Viterbi Algorithm; The Learning Problem and the Baum-Welch Algorithm; 1.3.2 Language models: N-grams; n-grams modelled by a stochastic finite state automaton; 1.3.3 Interactive Pattern Recognition; 1.3.4 Word-graphs; 1.4 Assessing Computer Assisted Transcription of Handwritten Text Images; 2. Corpora; 2.1 Introduction 327 $a2.2 CS2.3 ODEC; 2.4 IAMDB; 2.5 UNIPEN; 3. Handwritten Text Recognition; 3.1 Introduction; 3.2 Off-line Handwritten Text Recognition; 3.2.1 Preprocessing; 3.2.2 Feature Extraction; 3.2.3 Recognition; 3.2.4 Experimental Framework; 3.2.5 Meta-parameter Adjustment Experiments; 3.2.6 Discussion of Results; 3.3 On-line Handwritten Text Recognition; 3.3.1 Preprocessing; 3.3.2 Feature Extraction; 3.3.3 Recognition; 3.3.4 Experimental Framework; 3.3.5 Results; 3.4 Summary and Conclusions; 4. Computer Assisted Transcription of Handwritten Text Images; 4.1 Introduction; 4.2 Formal Framework 327 $a4.3 Adapting the Language Model4.4 Searching; 4.4.1 Direct Viterbi-based Approach; 4.4.2 Word-graph based Approach; 4.4.2.1 Error-correction parsing; 4.5 Increasing Interaction Ergonomy; 4.5.1 Language Modelling and Search; 4.6 Interacting at the Character Level; 4.6.1 Language Modelling and Search; 4.7 Experimental Framework; 4.7.1 Assessment Measures; 4.7.2 Parameters and Meta-Parameters; 4.8 Results; Direct Viterbi-based approach; Word-graph based approach; Using Pointer Actions in the CATTI interaction process (PA- CATTI); CATTI at the character level; 4.9 Conclusions and Future Work 327 $a5. Multimodal Computer Assisted Transcription of Handwritten Text Images5.1 Introduction; 5.2 Formal Framework; 5.3 Adapting the Language Model; 5.4 Searching; 5.5 Experimental Framework; 5.5.1 Corpora; 5.5.2 Assessment Measures; 5.6 Results; 5.7 Conclusions; 6. A Web-based Demonstrator of Interactive Multimodal Transcription; 6.1 Introduction; 6.2 User Interaction Protocol; 6.3 System Description; 6.3.1 Application Programming Interface; 6.3.2 MM-CATTI Server; 6.3.3 Web Interface; 6.3.4 Electronic Pen or Touchscreen Interaction; 6.3.5 Keyboard and Mouse Interaction; 6.4 Evaluation 327 $a6.4.1 Assessment Measures6.4.2 Corpus; 6.4.3 Participants; 6.4.4 Apparatus; 6.4.5 Procedure; 6.4.6 Design; 6.5 Results and Discussion; 6.5.1 Quantitative Analysis; 6.5.1.1 Analysis of Time; 6.5.1.2 Analysis of rWER; 6.5.1.3 Analysis of WSR; 6.5.2 Qualitative Analysis; 6.5.3 Correlation Analysis; 6.5.3.1 Correlation between trials; 6.5.3.2 Correlation between metrics; 6.5.4 Limitations of the Study; 6.6 Conclusions; 7. Conclusions and Outlook; 7.1 Conclusions; 7.2 Outlook; Acknowledgements; Appendix A Symbols and Acronyms; A.1 Symbols; A.2 Acronyms; Bibliography; Index 330 $aThis book presents an interactive multimodal approach for efficient transcription of handwritten text images. This approach, rather than full automation, assists the expert in the recognition and transcription process.Until now, handwritten text recognition (HTR) systems are far from being perfect and heavy human intervention is often required to check and correct the results of such systems. The interactive scenario studied in this book combines the efficiency of automatic handwriting recognition systems with the accuracy of the experts, leading to a cost-effective perfect transcription of th 410 0$aSERIES IN MACHINE PERCEPTION AND ARTIFICIAL INTELLIGENCE 606 $aWriting$xData processing 606 $aMultimodal user interfaces (Computer systems) 606 $aHuman-computer interaction 608 $aElectronic books. 615 0$aWriting$xData processing. 615 0$aMultimodal user interfaces (Computer systems) 615 0$aHuman-computer interaction. 676 $a006.425 700 $aRomer$b Vero?nica$0854914 701 $aToselli$b Alejandro He?ctor$0854915 701 $aVidal$b Enrique$042720 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910465481503321 996 $aMultimodal Interactive handwritten text transcription$91909043 997 $aUNINA