<p>Following Apache Tika <a href="https://tika.apache.org/1.17/index.html" rel="nofollow noreferrer">changelog</a> I came to <a href="https://issues.apache.org/jira/browse/TIKA-2262" rel="nofollow noreferrer">this</a> feature request for image captioning. There, the author stated that they used Google </p>

<blockquote>
  <p>'show and tell' neural network</p>
</blockquote>

<p>mentioned in <a href="https://ai.googleblog.com/2016/09/show-and-tell-image-captioning-open.html" rel="nofollow noreferrer">this</a> blog post.</p>

<p>Also, <a href="https://arxiv.org/abs/1609.06647" rel="nofollow noreferrer">here</a> is a link to the paper if you want to compare it to the current state of the art methods.</p>
What model does apache tika use internally - TensorflowRESTCaptioner
<p>Iam working on an image captioning tool and came across the apache tika</p>

<p><a href="https://tika.apache.org/1.18/api/org/apache/tika/parser/captioning/tf/TensorflowRESTCaptioner.html" rel="nofollow noreferrer">TensorflowRESTCaptioner</a></p>

<p>and would like to now which model does it use internally and how good are the results when compared with the state of the art right now in the market</p>

<p><a href="https://github.com/facebookresearch/pythia" rel="nofollow noreferrer">pythia - BUTF - FacebookResearch</a></p>