Description
This model is a Visual NER team aimed to extract the main key points in the summary page of SEC 10 K filings (Annual reports).
Predicted Entities
REGISTRANT, ADDRESS, PHONE, DATE, EMPLOYERIDNB, EXCHANGE, STATE, STOCKCLASS, STOCKVALUE, TRADINGSYMBOL, FILENUMBER
Live Demo Open in Colab Copy S3 URI
How to use
binary_to_image = BinaryToImage()\
.setInputCol("content") \
.setOutputCol("image") \
.setImageType(ImageType.TYPE_3BYTE_BGR)
img_to_hocr = ImageToHocr()\
.setInputCol("image")\
.setOutputCol("hocr")\
.setIgnoreResolution(False)\
.setOcrParams(["preserve_interword_spaces=0"])
tokenizer = HocrTokenizer()\
.setInputCol("hocr")\
.setOutputCol("token")
doc_ner = VisualDocumentNerV21()\
.pretrained("visualner_10kfilings", "en", "clinical/models")\
.setInputCols(["token", "image"])\
.setOutputCol("entities")
draw = ImageDrawAnnotations() \
.setInputCol("image") \
.setInputChunksCol("entities") \
.setOutputCol("image_with_annotations") \
.setFontSize(10) \
.setLineWidth(4)\
.setRectColor(Color.red)
# OCR pipeline
pipeline = PipelineModel(stages=[
binary_to_image,
img_to_hocr,
tokenizer,
doc_ner,
draw
])
bin_df = spark.read.format("binaryFile").load('data/t01.jpg')
results = pipeline.transform(bin_df).cache()
res = results.collect()
path_array = f.split(results['path'], '/')
results.withColumn('filename', path_array.getItem(f.size(path_array)- 1)) \
.withColumn("exploded_entities", f.explode("entities")) \
.select("filename", "exploded_entities") \
.show(truncate=False)
val binary_to_image = new BinaryToImage()
.setInputCol("content")
.setOutputCol("image")
.setImageType(ImageType.TYPE_3BYTE_BGR)
val img_to_hocr = new ImageToHocr()
.setInputCol("image")
.setOutputCol("hocr")
.setIgnoreResolution(False)
.setOcrParams(Array("preserve_interword_spaces=0"))
val tokenizer = new HocrTokenizer()
.setInputCol("hocr")
.setOutputCol("token")
val doc_ner = VisualDocumentNerV21()
.pretrained("visualner_10kfilings", "en", "clinical/models")
.setInputCols(Array("token", "image"))
.setOutputCol("entities")
val draw = new ImageDrawAnnotations()
.setInputCol("image")
.setInputChunksCol("entities")
.setOutputCol("image_with_annotations")
.setFontSize(10)
.setLineWidth(4)
.setRectColor(Color.red)
# OCR pipeline
val pipeline = new PipelineModel().setStages(Array(
binary_to_image,
img_to_hocr,
tokenizer,
doc_ner,
draw))
val bin_df = spark.read.format("binaryFile").load("data/t01.jpg")
val results = pipeline.transform(bin_df).cache()
val res = results.collect()
val path_array = f.split(results["path"], "/")
val results.withColumn("filename", path_array.getItem(f.size(path_array)- 1))
.withColumn(Array("exploded_entities", f.explode("entities")))
.select(Array("filename", "exploded_entities"))
.show(truncate=False)
Example
Output text
+--------+----------------------------------------------------------------------------------------------------------------------------------------------------------+
|filename|exploded_entities |
+--------+----------------------------------------------------------------------------------------------------------------------------------------------------------+
|t01.jpg |{named_entity, 712, 716, OTHERS, {confidence -> 96, width -> 74, x -> 1557, y -> 416, word -> Ended, token -> ended, height -> 18}, []} |
|t01.jpg |{named_entity, 718, 724, DATE-B, {confidence -> 96, width -> 97, x -> 1639, y -> 416, word -> January, token -> january, height -> 24}, []} |
|t01.jpg |{named_entity, 726, 727, DATE-I, {confidence -> 95, width -> 34, x -> 1743, y -> 416, word -> 31,, token -> 31, height -> 22}, []} |
|t01.jpg |{named_entity, 730, 733, DATE-I, {confidence -> 96, width -> 54, x -> 1785, y -> 416, word -> 2021, token -> 2021, height -> 18}, []} |
|t01.jpg |{named_entity, 735, 744, OTHERS, {confidence -> 91, width -> 143, x -> 1372, y -> 472, word -> Commission, token -> commission, height -> 18}, []} |
|t01.jpg |{named_entity, 746, 749, OTHERS, {confidence -> 96, width -> 36, x -> 1523, y -> 472, word -> file, token -> file, height -> 18}, []} |
|t01.jpg |{named_entity, 751, 756, OTHERS, {confidence -> 92, width -> 96, x -> 1568, y -> 472, word -> number:, token -> number, height -> 18}, []} |
|t01.jpg |{named_entity, 759, 761, FILENUMBER-B, {confidence -> 92, width -> 119, x -> 1675, y -> 472, word -> 001-39495, token -> 001, height -> 18}, []} |
|t01.jpg |{named_entity, 769, 773, REGISTRANT-B, {confidence -> 92, width -> 136, x -> 1472, y -> 558, word -> ASANA,, token -> asana, height -> 31}, []} |
|t01.jpg |{named_entity, 776, 778, REGISTRANT-I, {confidence -> 95, width -> 72, x -> 1620, y -> 558, word -> INC., token -> inc, height -> 25}, []}
+--------+----------------------------------------------------------------------------------------------------------------------------------------------------------+
Model Information
| Model Name: | visualner_10kfilings |
| Type: | ocr |
| Compatibility: | Visual NLP 4.0.0+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 744.4 MB |
References
SEC 10k filings