<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>dmezzetti</author><text>One additional library to add, if you&amp;#x27;re working with scientific papers: &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;github.com&amp;#x2F;kermitt2&amp;#x2F;grobid&quot;&gt;https:&amp;#x2F;&amp;#x2F;github.com&amp;#x2F;kermitt2&amp;#x2F;grobid&lt;/a&gt;. I use this with paperetl (&lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;github.com&amp;#x2F;neuml&amp;#x2F;paperetl&quot;&gt;https:&amp;#x2F;&amp;#x2F;github.com&amp;#x2F;neuml&amp;#x2F;paperetl&lt;/a&gt;).</text><parent_chain></parent_chain></comment><story><title>Show HN: Open-source Rule-based PDF parser for RAG</title><url>https://github.com/nlmatics/nlm-ingestor</url><text>The PDF parser is a rule based parser which uses text co-ordinates (boundary box), graphics and font data. The PDF parser works off text layer and also offers a OCR option to automatically use OCR if there are scanned pages in your PDFs. The OCR feature is based off a modified version of tika which uses tesseract underneath.&lt;p&gt;The PDF Parser offers the following features:&lt;p&gt;* Sections and subsections along with their levels.
* Paragraphs - combines lines.
* Links between sections and paragraphs.
* Tables along with the section the tables are found in.
* Lists and nested lists.
* Join content spread across pages.
* Removal of repeating headers and footers.
* Watermark removal.
* OCR with boundary boxes</text></story>
<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>epaga</author><text>This looks like it could be very helpful. The company I work for has a PDF comparison tool called &amp;quot;PDFC&amp;quot; which can read PDFs and runs comparisons of semantic differences. &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;www.inetsoftware.de&amp;#x2F;products&amp;#x2F;pdf-content-comparer&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;www.inetsoftware.de&amp;#x2F;products&amp;#x2F;pdf-content-comparer&lt;/a&gt;&lt;p&gt;Parsing PDFs can be quite the headache because the format is so complex. We support most of these features already but there are always so many edge cases that additional angles can be very helpful.</text><parent_chain></parent_chain></comment><story><title>Show HN: Open-source Rule-based PDF parser for RAG</title><url>https://github.com/nlmatics/nlm-ingestor</url><text>The PDF parser is a rule based parser which uses text co-ordinates (boundary box), graphics and font data. The PDF parser works off text layer and also offers a OCR option to automatically use OCR if there are scanned pages in your PDFs. The OCR feature is based off a modified version of tika which uses tesseract underneath.&lt;p&gt;The PDF Parser offers the following features:&lt;p&gt;* Sections and subsections along with their levels.
* Paragraphs - combines lines.
* Links between sections and paragraphs.
* Tables along with the section the tables are found in.
* Lists and nested lists.
* Join content spread across pages.
* Removal of repeating headers and footers.
* Watermark removal.
* OCR with boundary boxes</text></story>