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https://issues.apache.org/jira/browse/TIKA-2038?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15415126#comment-15415126
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Tim Allison commented on TIKA-2038:
-----------------------------------

bq. The performance on ICU4J improves dramatically if we strip the style/script 
info

Note that the ICU project's 
[documentation|http://userguide.icu-project.org/conversion/detection] 
recommends doing this:

bq. If you have more detailed knowledge about the structure of the input data, 
it is better to filter the data yourself before you pass it to CharsetDetector. 
For example, you might know that the data is from an html page that contains 
CSS styles, which will not be stripped by the input filter.

> A more accurate facility for detecting Charset Encoding of HTML documents
> -------------------------------------------------------------------------
>
>                 Key: TIKA-2038
>                 URL: https://issues.apache.org/jira/browse/TIKA-2038
>             Project: Tika
>          Issue Type: Improvement
>          Components: core, detector
>            Reporter: Shabanali Faghani
>            Priority: Minor
>         Attachments: comparisons_20160803b.xlsx, comparisons_20160804.xlsx, 
> iust_encodings.zip, tika_1_14-SNAPSHOT_encoding_detector.zip
>
>
> Currently, Tika uses icu4j for detecting charset encoding of HTML documents 
> as well as the other naturally text documents. But the accuracy of encoding 
> detector tools, including icu4j, in dealing with the HTML documents is 
> meaningfully less than from which the other text documents. Hence, in our 
> project I developed a library that works pretty well for HTML documents, 
> which is available here: https://github.com/shabanali-faghani/IUST-HTMLCharDet
> Since Tika is widely used with and within some of other Apache stuffs such as 
> Nutch, Lucene, Solr, etc. and these projects are strongly in connection with 
> the HTML documents, it seems that having such an facility in Tika also will 
> help them to become more accurate.



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