DL4JImageRecogniser for Further improvements

This model currently supports only importing VGG16 it will soon be able to support other moedels,
currently there is an issue with loading VGG16NOTOP due some bug in helperfunctions of DL4J.
The issue has been filed under issue as 

https://github.com/deeplearning4j/deeplearning4j/issues/3099

DL4JImageRecogniser Motivation

When ObjectRecognitionParser was built to do image recognition, there wasn't
good support for Java frameworks.  All the popular neural networks were in
C++ or python.  Since there was nothing that runs within JVM, we tried
several ways to glue them to Tika (like CLI, JNI, gRPC, REST).
However, this game is changing slowly now. Deeplearning4j, the most famous
neural network library for JVM, now supports importing models that are
pre-trained in python/C++ based kits [5].

*Improvement:*
It will be nice to have an implementation of ObjectRecogniser that
doesn't require any external setup(like installation of native libraries or
starting REST services). Reasons: easy to distribute and also to cut the IO
time.

  was:
When we built ObjectRecognitionParser to do image recognition, there wasn't
good support for Java frameworks.  All the popular neural networks were in
C++ or python.  Since there was nothing that runs within JVM, we tried
several ways to glue them to Tika (like CLI, JNI, gRPC, REST).
However, this game is changing slowly now. Deeplearning4j, the most famous
neural network library for JVM, now supports importing models that are
pre-trained in python/C++ based kits [5].

*Improvement:*
It will be nice to have an implementation of ObjectRecogniser that
doesn't require any external setup(like installation of native libraries or
starting REST services). Reasons: easy to distribute and also to cut the IO
time.

DL4JImageRecogniser Usage

The usage of this recogniser is very similar to TensorFlowRESTrecogniser but it doesn't require any external setup, like running  RESTservice in as in case of TensorFlowRESTrecogniser.
You can read more about TensorFlowRESTrecogniser at https://wiki.apache.org/tika/TikaAndVision

To use the DL4JImageRecogniser set
class param to org.apache.tika.parser.recognition.dl4j.DL4JImageRecogniser
modelType to VGG16
sample configuration is given below for refference. 
<?xml version="1.0" encoding="UTF-8"?>
<properties>
    <parsers>
        <parser class="org.apache.tika.parser.recognition.ObjectRecognitionParser">
            <mime>image/jpeg</mime>
            <params>
                <param name="topN" type="int">5</param>
                <param name="minConfidence" type="double">0.015</param>
                <param name="class" type="string">org.apache.tika.parser.recognition.dl4j.DL4JImageRecogniser</param>
				        <param name="modelType" type="string">VGG16</param> 
            </params>
        </parser>
    </parsers>
</properties>
Save the configuration at : tika-parsers/src/test/resources/org/apache/tika/parser/recognition/tika-config-tflow-rest

To run it, build the project and move to root directory of the project and run the command

java -Xmx3G -jar tika-app/target/tika-app-1.14.jar --config=tika-parsers/src/test/resources/org/apache/tika/parser/recognition/tika-config-tflow-rest.xml <path to your image file>

-Xmx3G is required because VGG16 model requires quite a lot of memory to run. If your system is not able to run it, you may try to pump up the memory further

Once the model runs, it automatically downloads the model file using helper functions of DL4J locally at .dl4j/trainedModels
To speed up the process in future, once the model is loaded from original hash files, it is serialized and saved on disk at .dl4j/trainedModels/tikaPreprocessed which significantly reduces
the resource usage (specially memory consumption) for future loads.