The Reader2Image annotator allows you to use the reading files with images more smoothly within existing Spark NLP workflows, enabling seamless reuse of your pipelines. Reader2Image can be used for extracting structured image content from various document types using Spark NLP readers. It supports reading from many files types and returns parsed output as a structured Spark DataFrame.

Supported formats include HTML and Markdown

Example

import com.johnsnowlabs.reader.Reader2Image
import com. johnsnowlabs.nlp.base.DocumentAssembler
import org.apache.spark.ml.Pipeline

val reader2Image = new Reader2Image()
  .setContentType("text/html")
  .setContentPath("./example-images.html")
  .setOutputCol("image")

val pipeline = new Pipeline()
  .setStages(Array(reader2Image))

val pipelineModel = pipeline.fit(emptyDataSet)
val resultDf = pipelineModel.transform(emptyDataSet)

resultDf.show()
+-------------------+--------------------+
|           fileName|               image|
+-------------------+--------------------+
|example-images.html|[{image, example-...|
|example-images.html|[{image, example-...|
+-------------------+--------------------+

resultDf.printSchema()

root
 |-- fileName: string (nullable = true)
 |-- image: array (nullable = false)
 |    |-- element: struct (containsNull = true)
 |    |    |-- annotatorType: string (nullable = true)
 |    |    |-- origin: string (nullable = true)
 |    |    |-- height: integer (nullable = false)
 |    |    |-- width: integer (nullable = false)
 |    |    |-- nChannels: integer (nullable = false)
 |    |    |-- mode: integer (nullable = false)
 |    |    |-- result: binary (nullable = true)
 |    |    |-- metadata: map (nullable = true)
 |    |    |    |-- key: string
 |    |    |    |-- value: string (valueContainsNull = true)
 |    |    |-- text: string (nullable = true)