Description
Document understanding transformer (Donut) model pretrained for Document Visual Question Answering (DocVQA) task in the dataset is from Document Visual Question Answering competition and consists of 50K questions defined on more than 12K documents. Donut is a new method of document understanding that utilizes an OCR-free end-to-end Transformer model. Donut does not require off-the-shelf OCR engines/APIs, yet it shows state-of-the-art performances on various visual document understanding tasks, such as visual document classification or information extraction (a.k.a. document parsing). Paper link OCR-free Document Understanding Transformer developed by Geewook Kim, Teakgyu Hong, Moonbin Yim, Jeongyeon Nam, Jinyoung Park, Jinyeong Yim, Wonseok Hwang, Sangdoo Yun, Dongyoon Han and Seunghyun Park. DocVQA seeks to inspire a “purpose-driven” point of view in Document Analysis and Recognition research, where the document content is extracted and used to respond to high-level tasks defined by the human consumers of this information.
Predicted Entities
Live Demo Open in Colab Copy S3 URI
How to use
binary_to_image = BinaryToImage()\
.setInputCol("content") \
.setOutputCol("image") \
.setImageType(ImageType.TYPE_3BYTE_BGR)
visual_question_answering = VisualQuestionAnswering()\
.pretrained("docvqa_donut_base", "en", "clinical/ocr")\
.setInputCol(["image"])\
.setOutputCol("answers")\
.setQuestionsCol("questions")
# OCR pipeline
pipeline = PipelineModel(stages=[
binary_to_image,
visual_question_answering
])
test_image_path = pkg_resources.resource_filename('sparkocr', 'resources/ocr/vqa/agenda.png')
bin_df = spark.read.format("binaryFile").load(test_image_path)
questions = [["When it finish the Coffee Break?", "Who is giving the Introductory Remarks?", "Who is going to take part of the individual interviews?"]]
questions_df = spark.createDataFrame([questions])
questions_df = questions_df.withColumnRenamed("_1", "questions")
image_and_questions = bin_df.join(questions_df)
results = pipeline.transform(image_and_questions).cache()
results.select(results.answers).show(truncate=False)
val binary_to_image = new BinaryToImage()
.setInputCol("content")
.setOutputCol("image")
.setImageType(ImageType.TYPE_3BYTE_BGR)
val visual_question_answering = VisualQuestionAnswering()
.pretrained("docvqa_donut_base", "en", "clinical/ocr")
.setInputCol(Array("image"))
.setOutputCol("answers")
.setQuestionsCol("questions")
# OCR pipeline
val pipeline = new PipelineModel().setStages(Array(
binary_to_image,
visual_question_answering))
val test_image_path = pkg_resources.resource_filename("sparkocr", "resources/ocr/vqa/agenda.png")
val bin_df = spark.read.format("binaryFile").load(test_image_path)
val questions = Array("When it finish the Coffee Break?", "Who is giving the Introductory Remarks?", "Who is going to take part of the individual interviews?")
val questions_df = spark.createDataFrame(Array(questions))
val questions_df = questions_df.withColumnRenamed("_1", "questions")
val image_and_questions = bin_df.join(questions_df)
val results = pipeline.transform(image_and_questions).cache()
results.select(results.answers).show(truncate=False)
Example
Input:
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|questions |
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|[ When it finish the Coffee Break?, Who is giving the Introductory Remarks?, Who is going to take part of the individual interviews?
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
Output:
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|answers |
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|[ When it finish the Coffee Break? -> 11:44 to 11:39 a.m., Who is giving the Introductory Remarks? -> lee a. waller, trrf vice presi- dent, Who is going to take part of the individual interviews? -> trrf treasurer]|
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
Model Information
| Model Name: | docvqa_donut_base |
| Type: | ocr |
| Compatibility: | Visual NLP 4.3.0+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |