Tika and Computer Vision
Contents
...
- Step 1. Install the dependencies
- Step 2. Create a Tika-Config XML to enable Tensorflow parser.
- Step 3: Demo
...
- Step 1. Setup REST Server
- Step 2. Create a Tika-Config XML to enable Tensorflow parser.
- Step 3. Demo
- Changing the default topN, API port or URL
...
| Table of Contents |
|---|
This page describes how to make use of Object (Visual) Recognition capability of Apache Tika. TIKA-1993 introduced a new parser to perform object recognition on images. Visit TIKA-1993 issue on Jira or pull request on Github to read the related conversation. The model was updated from Inception V3 to Inception V4 with TIKA-2306 (Pull request on Github). Continue reading to get Tika up and running for object recognition.
...
Description of parameters :
| Param Name | Type | Meaning | Range | Example |
| topN | int | Number of object names to output | a non-zero positive integer | 1 to receive top 1 object name |
| minConfidence | double | Minimum confidence required to output the name of detected objects | [0.0 to 1.0] inclusive | 0.9 for outputting object names iff at least 90% confident |
| class | string | Class that implements object recognition functionality | constant string | org.apache.tika.parser.recognition.tf.TensorflowImageRecParser |
Step 3: Demo
To use the vision capability via Tensorflow, just supply the above configuration to Tika.
...
Description of parameters :
| Param Name | Type | Meaning | Range | Example |
| topN | int | Number of object names to output | a non-zero positive integer | 1 to receive top 1 object name |
| minConfidence | double | Minimum confidence required to output the name of detected objects | [0.0 to 1.0] inclusive | 0.9 for outputting object names iff at least 90% confident |
| class | string | Name of class that Implements Object recognition Contract | constant string | org.apache.tika.parser.recognition.tf.TensorflowRESTRecogniser |
| healthUri | uri | HTTP URL to check availability of API service | any HTTP URL that gets 200 status code when available | http://localhost:8764/inception/v4/ping |
| apiUri | uri | HTTP URL to POST image data | any HTTP URL that returns data in the JSON format as shown in the sample API output | http://localhost:8764/inception/v4/classify/image?topk=10 |
Step 3. Demo
This demo is same as the Commandline Invocation approach, but this is faster and efficient
...