Thejan Wijesinghe created TIKA-2398:
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Summary: Unifying REST services
Key: TIKA-2398
URL: https://issues.apache.org/jira/browse/TIKA-2398
Project: Tika
Issue Type: Improvement
Components: parser
Affects Versions: 1.6
Reporter: Thejan Wijesinghe
h1. Background
Tika already has a [Object Recognition
Parser|https://wiki.apache.org/tika/TikaAndVision], a [Video Labeling
Parser|https://wiki.apache.org/tika/TikaAndVisionVideo] and an ongoing
[PR|https://github.com/apache/tika/pull/180] for an Image Captioning Parser.
All of these parsers are based on REST services, but currently there's no
convenient way users can deploy all these REST services, There can be use cases
such as user needs to use the Object Recognition Parser and the Image
Captioning parser together. The entire implementation will be based on docker.
Once implemented this, user will not have to build every single docker for
object recognition, video labeling, image captioning etc. etc. User will only
need to build the docker container which has this unified REST server and web
gui. This docker container will build other docker containers for image
captioning, object recognition etc. etc and host them as REST services whenever
user needs them. For an example, if a user needs to use object recognition
parser for the first time, he will have to only run this docker
container(unified REST server with web gui), then user can activate object
recognition service from the web gui. First time activating that service, it
will automatically create the docker container for object recognition, then
will make object recognition service available to the user.
h1. Objectives
# Automating the docker container building process for the user
# Creating a convenient platform for the user where he can start/terminate REST
services, see statistics of the model usage through a web gui
# Making the current REST services more stable using a reverse proxy server
h1. Benefits
# Convenience
# Easy to implement a new parser with deep learning capabilities by finding an
already trained NN model with any framework. i.e.framework doesn’t matter. We
don't need to stick to Keras, Tensorflow or DL4J. If we can host the service as
a web app. This platform can support it.
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