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ADVANCES IN INDUSTRIAL ENGINEERING AND MANAGEMENT
ISSN:2222-7059 (Print);EISSN: 2222-7067 (Online)
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Title : A New Approach to Information Retrieval based on Keyword Spotting from Handwritten Medical Prescriptions
Author(s) : Arghya Mukhejee, Arunit Halder, Subhrapratim Nath, Subir Kumar Sarkar
Author affiliation : 1Departments of CSE, Meghnad Saha Institute of Technology, Kolkata 700150, India
2Electronics & Telecommunication Engineering, Jadavpur University, Kolkata 700032, India
Corresponding author img Corresponding author at : Corresponding author img  

Abstract:
Handwriting recognition of medical prescriptions has been a challenging problem over the recent years with constant research in providing possible accurate solutions. Indecipherable handwritten prescription and inefficacy of Pharmacist to understand the medical prescription can lead to serious and harmful effect to the patients. In this paper, a document retrieval approach based on word spotting for medical prescriptions is proposed. The proposed approach involves extracting the domain specific knowledge of doctors by recognising printed text on letterhead part of the prescription in order to narrow down the search operation for word spotting in handwritten portion of the prescription. Handwritten word spotting performs on offline text portion using Hidden Markov Model. Experiment is performs on 500 prescriptions with a large heterogeneous medical data in varied environment to evaluate the effectiveness of the algorithm and thereby reveal the cognitive interpretation of Handwritten Prescriptions.

Key words:Handwritten recognition; Medical prescription; Word spotting; Line segmentation

Cite it:
Arghya Mukhejee, Arunit Halder, Subhrapratim Nath, Subir Kumar Sarkar, A New Approach to Information Retrieval based on Keyword Spotting from Handwritten Medical Prescriptions , Advances in Industrial Engineering and Management, vol. 6, no. 2, 2017, pp. 90-96, doi: 10.7508/aiem.2017.02.006

Full Text : PDF(size: 555.02 kB, 90-96, Download times:54)

DOI : 10.7508/aiem.2017.02.006

References:
[1]S. Young, 2006. The HTK Book, Version 3.4. CambridgeUniv. Eng. Dept.
[2]J. A. Rodríguez-Serrano, F. Perronnin 2009. Handwritten word-spotting using hidden Markov models and universal vocabularies. Pattern Recognition, vol. 42, no. 9, pp. 2106-2116.
[3]C. L. Liu, M. Koga and H, Fujisawa, 2002. Lexicon driven segmentation and recognition of handwritten character strings for Japanese address reading, IEEE T-PAMI, vol. 24, pp. 1425-1437.
[4]P. P. Roy, U. Pal, and J. Lladós, 2008. Morphology Based Handwritten Line Segmentation Using Foreground and Background Information”, Proc. International Conf. on Frontier of Handwriting Recognition, pp. 241-246.
[5]R. Jayadevan, S. R. Kolhe, P. M. Patil, U. Pal, 2012. Automatic processing of handwritten bank cheque images: a survey. IJDAR, vol. 15, no. 4, pp. 267-296.
[6]H. Bunke, 2003. Recognition of Cursive Roman Handwriting - Past, Present and Future. ICDAR, pp. 448-459.
[7]A. Fischer, A. Keller, V. Frinken, H. Bunke, 2012. Lexicon-free handwritten word spotting using character HMMs. Pattern Recognition Letters, vol. 33, no. 7, pp. 934-942.
[8]Q. Chen, T. Gong, L. Li, C. L. Tan, B. C. Pang, 2010. A Medical Knowledge Based Postprocessing Approach for Doctor's Handwriting Recognition. ICFHR, pp. 45-50
[9]T. M. Rath, R. Manmatha, 2007. Word spotting for historical documents”, IJDAR, vol. 9, no. 2-4), pp. 139-152.
[10]P. P. Roy, J. Y. Ramel, N. Ragot, 2011. Word Retrieval in Historical Document Using Character-Primitives. ICDAR, pp. 678-682.
[11]M. Rusiñol, D. Aldavert, R. Toledo, J. Lladós, 2015. Efficient segmentation-free keyword spotting in historical document collections. Pattern Recognition, vol. 48, no. 2, pp. 545-555.
[12]S. N. Srihari, E. J. Keubert, 1997. Integration of handwritten address interpretation technology into the United States Postal Service Remote Computer Reader System, ICDAR, pp. 892-896.
[13]Rothfeder, S. Feng, T. Rath, 2003. Using corner feature correspondences to rank word images by similarity, Workshop on Document Image Analysis and Retrieval.
[14]S. Wshah, G. Kumar, V. Govindaraju, 2014. Statistical script independent word spotting in offline handwritten documents. Pattern Recognition, vol. 47, no. 3, pp. 1039-1050.
[15]R. Milewski, V. Govindaraju, A. Bhardwaj, 2009. Automatic recognition of handwritten medical forms for search engines. IJDAR, vol. 11, no. 4, pp. 203-218.
[16]H. Cao, R. Prasad, P. Natarajan, 2011. Handwritten and Typewritten Text Identification and Recognition Using Hidden Markov Models, ICDAR, pp. 744-748.
[17]S. E. Boquera, M. J. C. Bleda, J. G. Moya, F. Z. Martínez, 2011. Improving Offline Handwritten Text Recognition with Hybrid HMM/ANN Models. IEEE T-PAMI, vol. 33, no. 4, pp. 767-779.
[18]D. Niyogi, S. N. Srihari, V. Govindaraju, 1997. Analysis of Printed Forms," Handbook of Character Recognition and Document Image Analysis, H. Bunke and P. S. P. Wang, eds., World Scientific Publishing, pp. 485-502.
[19]J. Guo and M. Ma, 2001. Separating handwritten material from machine printed text using Hidden Markov Models, In Proc. ICDAR, pp. 439-443.
[20]Y. Bai, L. Guo, L. Jin, Q. Huang, 2009. A novel feature extraction method using Pyramid Histogram of Orientation Gradients for smile recognition. ICIP, pp. 3305-3308.
[21]U. Marti and H. Bunke, 2002. The IAM-database: An English sentence database for off-line handwriting recognition, IJDAR, vol. 5, pp. 39-46.
[22]S. N. Srihari, C.Huang, H.Srinivasan, 2005. A search engine for handwritten documents, Document Recognition and Retrieval, pp. 66-75.
[23]B. Zhang, S. N. Srihari, 2003. Binary vector dissimilarity measures for handwriting identification, in: Document Recognition and Retrieval, pp. 28-38.

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