ImageHandwrittenDetector
ImageHandwrittenDetector is a DL model for detect handwritten text on the image.
It based on Cascade Region-based CNN network.
Detector support following labels:
- ‘signature’
- ‘date’
- ‘name’
- ‘title’
- ‘address’
- ‘others’
Input Columns
| Param name | Type | Default | Column Data Description |
|---|---|---|---|
| inputCol | string | image | image struct (Image schema) |
Parameters
| Param name | Type | Default | Description |
|---|---|---|---|
| scoreThreshold | float | 0.5 | Score threshold for output regions. |
| outputLabels | Array[String] | White list for output labels. | |
| labels | Array[String] | List of labels |
Output Columns
| Param name | Type | Default | Column Data Description |
|---|---|---|---|
| outputCol | string | table_regions | array of [Coordinaties]ocr_structures#coordinate-schema) |
Example:
import com.johnsnowlabs.ocr.transformers.*
import com.johnsnowlabs.ocr.OcrContext.implicits._
val imagePath = "path to image"
// Read image file as binary file
val df = spark.read
.format("binaryFile")
.load(imagePath)
.asImage("image")
// Define transformer for detect signature
val signature_detector = ImageHandwrittenDetector
.pretrained("image_signature_detector_gsa0628", "en", "public/ocr/models")
.setInputCol("image")
.setOutputCol("signature_regions")
val draw_regions = new ImageDrawRegions()
.setInputCol("image")
.setInputRegionsCol("signature_regions")
.setOutputCol("image_with_regions")
pipeline = PipelineModel(stages=[
binary_to_image,
signature_detector,
draw_regions
])
val data = pipeline.transform(df)
data.storeImage("image_with_regions")
from pyspark.ml import PipelineModel
from sparkocr.transformers import *
imagePath = "path to image"
# Read image file as binary file
df = spark.read
.format("binaryFile")
.load(imagePath)
binary_to_image = BinaryToImage() \
.setInputCol("content") \
.setOutputCol("image")
# Define transformer for detect signature
signature_detector = ImageHandwrittenDetector \
.pretrained("image_signature_detector_gsa0628", "en", "public/ocr/models") \
.setInputCol("image") \
.setOutputCol("signature_regions")
draw_regions = ImageDrawRegions() \
.setInputCol("image") \
.setInputRegionsCol("signature_regions") \
.setOutputCol("image_with_regions")
pipeline = PipelineModel(stages=[
binary_to_image,
signature_detector,
draw_regions
])
data = pipeline.transform(df)
display_images(data, "image_with_regions")
Output:
