BungeniX - XML Conversion Engine
Do you have legal documents in PDF, Word or Text format that you want to convert to Akoma Ntoso XML ? We have an automated process. See the online demos: server side converter, client-side simple and client-side complex. Read below for more details
Bungeni Consulting's specialized parser mapped to the Akoma Ntoso XML standard allows easy conversion of plain text, word or PDF documents into Akoma Ntoso XML documents. Our design provides entry points to plug-in custom processing during the conversion process; the text document is converted to a semantic JSON format which can be processed further if need be by intermediate custom processing before final conversion to XML.
Bungeni Consulting's specialized parser mapped to the Akoma Ntoso XML standard allows easy conversion of plain text, word or PDF documents into Akoma Ntoso XML documents. Our design provides entry points to plug-in custom processing during the conversion process; the text document is converted to a semantic JSON format which can be processed further if need be by intermediate custom processing before final conversion to XML.
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STEP 1. Process Text
We prepare the text to a consistent format, for the converter. |
STEP 2. Semantic JSON
We parse the text into a semantic JSON object which can be processed further if desired. |
STEP 3. Akoma Ntoso XML
The Semantic JSON is converted to Akoma Ntoso XML. |
1. TEXT DOCUMENT
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 2. INTERMEDIATE JSON
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3. AKOMA NTOSO XML
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Click here for a live demo of the parser
Our software imports Tables, Images, Links, Footnotes in Word documents automatically into Akoma Ntoso.
Our parser converter design allows using the same parser to handle text from different languages. This allows us to parse and identify semantic metadata in the text across the same document published in different languages.
For instance, in an english document a date appears as "12th January 2011", and in the corresponding Spanish translation of the document the date may appear as "12 de enero de 2011", but both need to identified as a semantic machine readable date "2011-01-12"; similarly the city Johannesburg appears as "Johannesburg" in an English document, and in the corresponding Portuguese document as "Joanesburgo", and "جوهانسبرج" in the Arabic translation. All three of these names need to be semantically tagged as the same location "Johannesburg" - the parser identifies such semantics across languages.
Our parser converter design allows using the same parser to handle text from different languages. This allows us to parse and identify semantic metadata in the text across the same document published in different languages.
For instance, in an english document a date appears as "12th January 2011", and in the corresponding Spanish translation of the document the date may appear as "12 de enero de 2011", but both need to identified as a semantic machine readable date "2011-01-12"; similarly the city Johannesburg appears as "Johannesburg" in an English document, and in the corresponding Portuguese document as "Joanesburgo", and "جوهانسبرج" in the Arabic translation. All three of these names need to be semantically tagged as the same location "Johannesburg" - the parser identifies such semantics across languages.
SEMANTIC METADATA