Indexing and retrieval of handwritten medical forms

ABSTRACT

POSTER PAPER. This paper proposes an approach of indexing and retrieving degraded handwritten documents. We present a modified version of the popular Vector Model in information retrieval (IR). Our model incorporates top n candidates from a HR system into the scheme of calculating the term frequency (tf) and the inverted document frequency (idf). Standardized IR Tests show that the proposed approach outperforms the retrieval of ordinary HR text in terms of mean average precision (MAP) and R-Precision.

REFERENCES

• 1: Hongyan Jing, Using hidden Markov modeling to decompose human-written summaries, Computational Linguistics, v.28 n.4, p.527-543, December 2002 [doi>10.1162/089120102762671972]
• 2: Gyeonghwan Kim , Venu Govindaraju, A Lexicon Driven Approach to Handwritten Word Recognition for Real-Time Applications, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.19 n.4, p.366-379, April 1997 [doi>10.1109/34.588017]
• 3: R. Milewski and V. Govindaraju. Extraction of handwritten text from carbon copy medical form images. In Document Analysis Systems, pages 106--116, 2006.
• 4: E. Mittendorf and P. Schauble. Measuring the effects of data corruption on information retrieval. In Proceedings of the Fifth Annual Symposium on Document Analysis and Information Retrieval, 1996.
• 5: Manabu Ohta , Atsuhiro Takasu , Jun Adachi, Retrieval methods for English-text with missrecognized OCR characters, Proceedings of the 4th International Conference on Document Analysis and Recognition, p.950-956, August 18-20, 1997

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.5 PATTERN RECOGNITION

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL

General Terms:
Algorithms, Experimentation

Keywords:
OCR, information retrieval