Load & Predict 1 liner
The johnsnowlabs library provides 2 simple methods with which most visual NLP tasks can be solved while achieving state-of-the-art results.
The load and predict method.
When building a load&predict based model you will follow these steps:
- Pick a visual model/pipeline/component you want to create from the Namespace
- Call the
model = ocr.load('visual_component')method which will return an auto-completed pipeline - Call
model.predict('path/to/image.png')with a path to a file or an array of paths
These 3 steps can be boiled down to just 1 line
from johnsnowlabs import nlp
nlp.load('img2text').predict('path/to/haiku.png')
nlp.load() defines 6 visual components types usable in 1-liners
| 1-liner | Transformer Class |
|---|---|
nlp.load('img2text').predict('path/to/cat.png') |
ImageToText |
nlp.load('pdf2text').predict('path/to/taxes.pdf') |
PdfToText |
nlp.load('doc2text').predict('path/to/my_homework.docx') |
DocToText |
nlp.load('pdf2table').predict('path/to/data_tables.pdf') |
PdfToTextTable |
nlp.load('ppt2table').predict('path/to/great_presentation_with_tabular_data.pptx') |
PptToTextTable |
nlp.load('doc2table').predict('path/to/tabular_income_data.docx') |
DocToTextTable |
Custom Pipelines
You can create Visual Annotator & PretrainedPipeline based pipelines using all the classes
attached to the visual module which gives you the highest degree of freedom
from johnsnowlabs import nlp,visual
spark = nlp.start(visual=True)
# Load a PDF File and convert it into Spark DF format
doc_example = visual.pkg_resources.resource_filename('sparkocr', 'resources/ocr/docs/doc2.docx')
doc_example_df = spark.read.format("binaryFile").load(doc_example).cache()
# Run the visual DocToText Annotator inside a pipe, recognize text and show the result
pipe = nlp.PipelineModel(stages=[visual.DocToText().setInputCol("content").setOutputCol("text")])
result = pipe.transform(doc_example_df)
print(result.take(1)[0].text)
output:
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