05778nam 2200721Ia 450 991080792410332120200520144314.01-283-59373-49786613906182981-4390-34-8(CKB)2560000000093359(EBL)1019616(OCoLC)811504361(SSID)ssj0000703140(PQKBManifestationID)12278738(PQKBTitleCode)TC0000703140(PQKBWorkID)10689665(PQKB)10204807(MiAaPQ)EBC1019616(WSP)00002777(Au-PeEL)EBL1019616(CaPaEBR)ebr10596916(CaONFJC)MIL390618(OCoLC)810933234(EXLCZ)99256000000009335920120111d2012 uy 0engur|n|---|||||txtccrMultimodal Interactive handwritten text transcription /Veronica Romero, Alejandra Hector Toselli, Enrique Vidal1st ed.Singapore ;Hackensack, NJ World Scientific Pubc20121 online resource (180 p.)Series in machine perception and artificial intelligence ;v. 80Description based upon print version of record.981-4390-33-X Includes bibliographical reference (p. 155-164) and index.Contents; 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 Introduction2.2 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 Framework4.3 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 Work5. 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 Evaluation6.4.1 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; IndexThis 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 thSERIES IN MACHINE PERCEPTION AND ARTIFICIAL INTELLIGENCEWritingData processingMultimodal user interfaces (Computer systems)Human-computer interactionWritingData processing.Multimodal user interfaces (Computer systems)Human-computer interaction.006.425Romer Veronica1656960Toselli Alejandro Hector1656961Vidal Enrique42720MiAaPQMiAaPQMiAaPQBOOK9910807924103321Multimodal Interactive handwritten text transcription4010114UNINA