Text recognition systems

Also databases of scientific literature (such as PUBMED, MEDLINE) provide additional functionality, e.g. they can search for similar articles based on word-usage analysis. Text recognition systems are being developed that automatically extract knowledge about protein function from the abstracts of scientific articles, notably on protein-protein interactions. 

Bunescu et al. compared the ability of several machine learning systems to extract information regarding protein names and their interactions from Medline abstracts.The text recognition systems compared are dictionary based, the rule learning system Rapier, boosted wrapper induction, SVM, maximum entropy, / -nearest neighbors, and two systems for protein name identification, KEX and Abgene. Based on the F-measure (harmonic mean of precision and recall) in L10%O cross-validation, the best systems for protein name recognition are the maximum entropy with dictionary (F = 57.86%) followed by SVM with dictionary (F = 54.42%). 

Vector Quantization (VQ) technique is used in speaker recognition system. This technique is easy to implement through the use of the Euclidean distance measure. However, it provides less accuracy in terms of performance if only this algorithm was used for the speaker verification system. This algorithm for VQ is base on [3] with large data training required. They used 100 speakers and able to achieve 56% performance improvement using text dependent mode compared to independent. 

Rosenbeg, A., Soong, F., (1987 Septemba-). Evaluation of a Vector Quantization Talker Recognition System in Text Independent and Text Dependent and Text Dependent Modes. Pages 143-157 of Computer Speech and language, vol.22. 

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Thus, a system for the recognition of medical entities in text has to offer functionalities beyond simple string matching—namely, context-dependent disambiguation—to support the mapping of entities in text to concepts in medical ontologies. 

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