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Key | Notes |
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Content-Type | This is the file's mime type as identified by Tika. Example: application/pdf |
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X-TIKA:digest:MD5 | If you've configured digests, they are returned with a key of the form X-TIKA:digest:ALGORITHM. |
resourceName | File name |
Content-Length | When available, the number of bytes in a stream |
X-TIKA:content | This is the text that is extracted from the files |
X-TIKA:content_handler | This is the content handler that was used for handling the text (e.g. Text, XHTML, etc.) |
X-TIKA:embedded_resource_path |
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X-TIKA:embedded_depth |
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X-TIKA:encrypted | If a parser throws an EncryptedDocumentException, the parser also sets this value to true in the metadata. |
tika:file_ext | File extension |
...
Format Specific Metadata
PDF Metadata
PDF metadata is typically stored via two mechanisms, one is the "native" PDF docinfo
metadata object and the other is via XMP. For cases where there may be the same key, e.g. "created," in both the docinfo and the XMP, Tika reports the information in the XMP. In this case, the created date in the XMP would be reported as dcterms:created
.
Some users want to extract the literal docinfo
information (irrespective of the XMP), and for that Tika prefixes keys with pdf:docinfo
.
Note that XMP metadata may have custom keys, and some PDFs store custom metadata in the docinfo.
PDF is a "page-based" file format, and the number of pages is stored in xmpTPg:NPages
.
Key | Notes |
---|
access_permission:assemble_document |
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access_permission:can_modify |
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access_permission:can_print |
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access_permission:can_print_degraded |
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access_permission:extract_content |
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access_permission:extract_for_accessibility |
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access_permission:fill_in_form |
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access_permission:modify_annotations |
|
pdf:actionTrigger |
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pdf:annotationSubtypes |
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pdf:annotationTypes |
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pdf:charsPerPage |
|
pdf:docinfo:custom:* | Custom metadata stored in the docinfo dictionary, e.g. pdf:docinfo:custom:_dlc_policyId |
pdf:docinfo:created |
|
pdf:docinfo: | custom:Companypdf:docinfo:custom:SourceModifiedcreator |
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pdf:docinfo:creator_tool |
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pdf:docinfo:keywords |
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pdf:docinfo:modified |
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pdf:docinfo:producer |
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pdf:docinfo:title |
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pdf:docinfo:trapped |
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pdf:has3D |
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pdf:hasAcroFormFields |
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pdf:hasCollection |
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pdf:hasMarkedContent |
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pdf:hasXFA |
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pdf:hasXMP |
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pdf:PDFExtensionVersion |
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pdf:PDFVersion |
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pdf:producer |
|
pdf:unmappedUnicodeCharsPerPage |
|
pdfa:PDFVersion |
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pdfaid:conformance |
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pdfaid:part |
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pdfuaid:part |
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pdfvt:modified |
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pdfvt:version |
|
pdfx:conformance |
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pdfx:version |
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pdfxid:version |
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Microsoft Office Files
Key | Notes |
---|
embeddedRelationshipId |
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RTF Files
Key | Notes |
---|
rtf_meta:emb_app_version |
|
rtf_meta:emb_class |
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rtf_meta:thumbnail |
|
rtf_pict:* | metadata around embedded images in RTF. A few examples include: rtf_pict:borderLeftColor, rtf_pict:borderRightColor, rtf_pict:borderTopColor, rtf_pict:dhgt, rtf_pict:dxHeightHR, rtf_pict:dxTextLeft, rtf_pict:dxTextRight, rtf_pict:dxWidthHR |
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Tiff Files
Key | Notes |
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tiff:ImageWidth |
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tiff:ImageLength |
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tiff:BitsPerSample |
|
Exif Keys
Key | Notes |
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Exif SubIFD:Metering Mode |
|
Exif SubIFD:White Balance Mode |
|
Exif SubIFD:Scene Capture Type |
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Exif SubIFD:Exposure Mode |
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Text/Html-based Files
Tool Specific Metadata
Tika Eval
To get this metadata, you need to have the tika-eval-core jar on your class path.
Key | Notes |
---|
tika-eval:numTokens | Number of tokens (words) in the extracted text. |
tika-eval:numUniqueTokens | Number of unique tokens (words), when used with the numTokens, useful for measuring vocabulary richness/repetition |
tika-eval:numAlphaTokens | Number of alphabetic tokens |
tika-eval:numUniqueAlphaTokens | Number of unique alphabetic tokens |
tika-eval:lang | Language automatically detected by Tika's modified OpenNLP language detector |
tika-eval:langConfidence | Confidence of that language |
tika-eval:oov | Out of vocabulary statistic. The tika-eval module has lists of the top 20k most common words for each of 120+ languages. Based on the detected languages, the number of "common tokens" is divided by the number of alphabetic tokens, we then subtract this value from 1 to calculate the percentage of words that are not in the top 20k "common words" for the identified language. This is very helpful for junk detection (identifying when text extraction failed) and for comparing the output of two parsers. See Popat's paper. |
Siegfried Detector
To extract Siegfried detection information, you have to have Siegfried commandline application installed (and callable as "sf" on the commandline) and you need to add the tika-detector-siegfried jar to your class path.
Key | Notes |
---|
sf:pronom:mime |
|
sf:pronom:format |
|
sf:pronom:version |
|
sf:pronom:id |
|
sf:pronom:basis |
|
sf:errors |
|