Segmentation of english Offline handwritten cursive scripts using a feedforward neural network (original) (raw)

References

  1. Salvi D, Zhou J, Waggoner J, Wang S (2013) Handwritten text segmentation using average longest path algorithm. In: Workshop on applications of computer vision (WACV), IEEE, ISBN 978-1-4673-5053-2, pp 505–512
  2. Sarkar R, Halder S, Malakar S, Das N, Basu S, Nasipuri M (2012) Text line extraction from handwritten document pages based on line contour estimation. In: 3rd international conference on computing communication and networking technologies (ICCCNT), IEEE, INSPEC 13252116, pp 1–8
  3. Impedovo S, Facchini G, Mangini FM (2012) A new cursive basic word database for bank-check processing systems. In: 10th IAPR international workshop on document analysis systems (DAS), IEEE, ISBN 978-1-4673-0868-7, pp 450–454
  4. Lee H, Verma B (2010) Over-segmentation and neural binary validation for cursive handwriting recognition. In: International joint conference on neural networks (IJCNN), IEEE, ISBN 978-1-4244-6916-1, pp 1–5
  5. Luthy F, Varga T, Bunke H (2007) Using hidden markov models as a tool for handwritten text line segmentation. In: Ninth international conference on document analysis and recognition (ICDAR 2007), IEEE, ISBN 978-0-7695-2822-9, p 8
  6. Njah S, Nouma BB, Bezine H, Alimi AM (2012) MAYASTROUN: Multilanguage handwriting database. In: International conference on frontiers in handwriting recognition (ICFHR), IEEE, ISBN 978-1-4673-2262-1, pp 308–312
  7. Shivram A, Zhu B, Setlur S, Nkagawa M (2013) Segmentation-based online word recognition: a conditional random field driven beam search strategy. In: 12th International conference on document analysis and recognition (ICDAR), IEEE, ISSN 1520-5363, pp 852–856
  8. Zhu B, Shivram A, Setlur S, Govindaraju V, Nakagawa M (2013) Online handwritten cursive word recognition using segmentation-free MRF in combination with P2DBMN-MQDF. In: 12th international conference on document analysis and recognition (ICDAR), IEEE, ISSN 1520-5363, pp 349–353
  9. Bezerra BLD, Zanchettin C, Bragad de Andrade V (2012) A hybrid RNN Model for cursive offline handwriting recognition.In: Brazilian symposium on neural networks (SBRN), IEEE, ISBN 978-1-4673-2641-4, pp 113–118
  10. Stamatopoulos N, Gatos B, Louloudis G, Pal U (2013) Handwriting segmentation contest. In: 12th international conference on document analysis and recognition (ICDAR), IEEE, ISSN 1520-5363, pp 1402–1406
  11. Pradeep J, Srinivasan E, Himavathi S (2012) Performance analysis of hybrid feature extraction technique for recognizing English handwritten characters. In: World Congress on Information and Communication Technologies (WICT), IEEE, ISBN 978-1-4673-4806-5, pp 373–377
  12. Yuan A, Bai G, Yang P, Guo Y (2012) Handwritten english word recognition based on convolution neural networks. In: International conference on frontiers in handwriting recognition (ICFHR), IEEE, ISBN 978-1-4673-2262-1, pp 207–212
  13. Gilliam T, Wilson RC, Clark John A (2011) Segmentation and normalisation in grapheme codebooks. In: International conference on document analysis and recognition (ICDAR), IEEE, ISBN 978-1-4577-1350-7, pp 613–61
  14. Ryu J, Koo HI, Cho NI (2015) Word segmentation method for handwritten documents based on structured learning. IEEE Trans Signal Process Lett 22(8)
  15. Sharma MK, Dhaka VP (2015) An efficient segmentation technique for Devanagari offline handwritten scripts using the Feedforward Neural Network. Neural Comput Appl. doi:10.1007/s00521-015-1844-9
  16. Dhaka VP, Sharma MK (2015) Classification of image using a genetic general neural decision treeInt. J Appl Pattern Recogn 21:76–95
    Article Google Scholar
  17. Obaidullah SM, Mondal A, Roy K (2014) Structural feature-based approach for script identification from printed Indian document. In: IEEE international conference on signal processing and integrated networks (SPIN), pp 120–124
  18. Zhu G (2009) Language and Media Process. Laboratory, University of Maryland, College Park, MD, USA. In: Zheng Y, Doermann D, Jaeger S (eds) Signature detection and matching for document image retrieval, IEEE transaction on pattern analysis and machine intelligence, 31(11): 2015–2031
  19. You J (2003) Department of Computer, Hong Kong Polytechnic University, Kowloon. In: Zhang D, Cao J, Guo M (eds) Parallel biometrics computing using mobile agents, IEEE international conference on parallel processing, pp 305–312
  20. Pervouchine V (2005) School of Computer Engineering, Nanyang Technology University, Singapore. In: Leedham G (ed) Document examiner feature extraction: thinned vs. skeletonised handwriting images, TENCON 2005 IEEE Region 10, pp 1–6
  21. Tomai CI (2004) Department of computer science and engineering, center of excellence for document analysis and recognition, Amherst, MA, USA. In: Zhang B, Srihari SN (eds) Discriminatory power of handwritten words for writer recognition”, Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, vol 2, pp 638–641
  22. Jiang Y, Ding X, Fu Q, Ren Z (2006) Context driven chinese string segmentation and recognition. In: Proceedings structural, syntactic, and statistical pattern recognition: joint IAPR Int’l Workshops, pp 127–135
  23. Gatos B, Louloudis G, Pratikakis I, Halatsis C (2009) Text line and word segmentation ofhandwritten documents. Pattern Recogn 42(12):3169–3183
    Article MATH Google Scholar
  24. Stafylakis T, Papavassiliou V, Katsouros V, Carayann G (2010) Handwritten document image segmentation into text lines and words. Pattern Recogn 43(1):369–377
    Article MATH Google Scholar
  25. Sharma MK, Dhaka VP (2015) Pixel plot and trace based segmentation method for bilingual handwritten scripts using feedforward neural network. Neural Comput Appl. doi:10.1007/s00521-015-1972-2

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