ImageHandwrittenDetector
ImageHandwrittenDetector is a DL model for detect handwritten text on the image.
It’s 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:
ImageTextDetector
ImageTextDetector is a DL model for detecting text on the image.
It’s based on CRAFT network architecture.
Input Columns
| Param name | Type | Default | Column Data Description |
|---|---|---|---|
| inputCol | string | image | image struct (Image schema) |
Parameters
| Param name | Type | Default | Description |
|---|---|---|---|
| scoreThreshold | float | 0.9 | Score threshold for output regions. |
| sizeThreshold | int | 5 | Size threshold for detected text |
| textThreshold | float | 0.4f | Text threshold |
| linkThreshold | float | 0.4f | Link threshold |
| width | integer | 0 | Scale width to this value, if 0 use original width |
| height | integer | 0 | Scale height to this value, if 0 use original height |
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 text
val text_detector = ImageTextDetector
.pretrained("text_detection_v1", "en", "clinical/ocr")
.setInputCol("image")
.setOutputCol("text_regions")
val draw_regions = new ImageTextDetector()
.setInputCol("image")
.setInputRegionsCol("text_regions")
.setOutputCol("image_with_regions")
.setSizeThreshold(10)
.setScoreThreshold(0.9)
.setLinkThreshold(0.4)
.setTextThreshold(0.2)
.setWidth(1512)
.setHeight(2016)
pipeline = PipelineModel(stages=[
binary_to_image,
text_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 text
text_detector = ImageTextDetector \
.pretrained("text_detection_v1", "en", "clinical/ocr") \
.setInputCol("image") \
.setOutputCol("text_regions") \
.setSizeThreshold(10) \
.setScoreThreshold(0.9) \
.setLinkThreshold(0.4) \
.setTextThreshold(0.2) \
.setWidth(1512) \
.setHeight(2016)
draw_regions = ImageDrawRegions() \
.setInputCol("image") \
.setInputRegionsCol("text_regions") \
.setOutputCol("image_with_regions")
pipeline = PipelineModel(stages=[
binary_to_image,
text_detector,
draw_regions
])
data = pipeline.transform(df)
display_images(data, "image_with_regions")
Output:
ImageTextDetectorV2
ImageTextDetectorV2 is a DL model for detecting text on images.
It is based on the CRAFT network architecture with refiner net. Refiner net
runs as postprocessing, and is able to merge single words regions into lines.
Currently, it’s available only on Python side.
Input Columns
| Param name | Type | Default | Column Data Description |
|---|---|---|---|
| inputCol | string | image | image struct (Image schema) |
Parameters
| Param name | Type | Default | Description |
|---|---|---|---|
| scoreThreshold | float | 0.7 | Score threshold for output regions. |
| sizeThreshold | int | 10 | Threshold for height of the detected regions. |
| textThreshold | float | 0.4f | Threshold for the region(text) score. The region score represents the probability that the given pixel is the center of the character. |
| linkThreshold | float | 0.4f | Threshold for the the link(affinity) score. The link score represents the center probability of the space between adjacent characters. |
| width | integer | 1280 | Width of the desired input image. Image will be resized to this width. |
| withRefiner | boolean | false | Enable to run refiner net as postprocessing step. |
Output Columns
| Param name | Type | Default | Column Data Description |
|---|---|---|---|
| outputCol | string | table_regions | array of [Coordinaties]ocr_structures#coordinate-schema) |
Example:
not implemented
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 text
text_detector = ImageTextDetectorV2 \
.pretrained("image_text_detector_v2", "en", "clinical/ocr") \
.setInputCol("image") \
.setOutputCol("text_regions") \
.setScoreThreshold(0.5) \
.setTextThreshold(0.2) \
.setSizeThreshold(10) \
.setWithRefiner(True)
draw_regions = ImageDrawRegions() \
.setInputCol("image") \
.setInputRegionsCol("text_regions") \
.setOutputCol("image_with_regions")
pipeline = PipelineModel(stages=[
binary_to_image,
text_detector,
draw_regions
])
data = pipeline.transform(df)
display_images(data, "image_with_regions")
