Description
This is a Form Recognition / Key Value extraction model, trained on the summary page of SEC 10K filings. It extracts KEY, VALUE or HEADER as entities, being HEADER the title on the filing.
Predicted Entities
KEY, VALUE, HEADER
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_keyvalue_10kfilings", "en", "clinical/ocr")\
.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_keyvalue_10kfilings", "en", "clinical/ocr")
.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, 268, 269, OTHERS, {confidence -> 96, width -> 14, x -> 822, y -> 1101, word -> of, token -> of, height -> 34}, []} |
|t01.jpg |{named_entity, 271, 273, OTHERS, {confidence -> 89, width -> 33, x -> 837, y -> 1112, word -> the, token -> the, height -> 13}, []} |
|t01.jpg |{named_entity, 275, 277, OTHERS, {confidence -> 89, width -> 30, x -> 874, y -> 1113, word -> Act., token -> act, height -> 12}, []} |
|t01.jpg |{named_entity, 280, 282, KEY-B, {confidence -> 94, width -> 26, x -> 910, y -> 1113, word -> Yes, token -> yes, height -> 12}, []} |
|t01.jpg |{named_entity, 284, 285, VALUE-B, {confidence -> 45, width -> 13, x -> 944, y -> 1112, word -> LI, token -> li, height -> 13}, []} |
|t01.jpg |{named_entity, 287, 288, KEY-B, {confidence -> 83, width -> 22, x -> 963, y -> 1113, word -> No, token -> no, height -> 12}, []} |
|t01.jpg |{named_entity, 290, 295, HEADER-B, {confidence -> 96, width -> 89, x -> 1493, y -> 13, word -> UNITED, token -> united, height -> 16}, []} |
|t01.jpg |{named_entity, 297, 302, HEADER-I, {confidence -> 95, width -> 83, x -> 1590, y -> 13, word -> STATES, token -> states, height -> 16}, []} |
|t01.jpg |{named_entity, 304, 313, HEADER-B, {confidence -> 95, width -> 221, x -> 1186, y -> 45, word -> SECURITIES, token -> securities, height -> 25}, []} |
|t01.jpg |{named_entity, 315, 317, HEADER-I, {confidence -> 95, width -> 80, x -> 1415, y -> 45, word -> AND, token -> and, height -> 25}, []} |
|t01.jpg |{named_entity, 319, 326, HEADER-I, {confidence -> 96, width -> 212, x -> 1507, y -> 45, word -> EXCHANGE, token -> exchange, height -> 25}, []} |
|t01.jpg |{named_entity, 328, 337, HEADER-I, {confidence -> 95, width -> 249, x -> 1732, y -> 45, word -> COMMISSION, token -> commission, height -> 25}, []} |
|t01.jpg |{named_entity, 339, 348, HEADER-B, {confidence -> 96, width -> 125, x -> 1461, y -> 86, word -> Washington,, token -> washington, height -> 21}, []} |
|t01.jpg |{named_entity, 351, 351, HEADER-I, {confidence -> 93, width -> 43, x -> 1595, y -> 86, word -> D.C., token -> d, height -> 16}, []} |
|t01.jpg |{named_entity, 356, 360, HEADER-I, {confidence -> 93, width -> 59, x -> 1646, y -> 86, word -> 20549, token -> 20549, height -> 16}, []} |
|t01.jpg |{named_entity, 362, 365, HEADER-B, {confidence -> 93, width -> 112, x -> 1484, y -> 159, word -> FORM, token -> form, height -> 25}, []} |
|t01.jpg |{named_entity, 367, 368, HEADER-I, {confidence -> 91, width -> 77, x -> 1609, y -> 159, word -> 10-K, token -> 10, height -> 25}, []} |
+--------+---------------------------------------------------------------------------------------------------------------------------------------------------------+
Model Information
| Model Name: | visualner_keyvalue_10kfilings |
| Type: | ocr |
| Compatibility: | Visual NLP 4.0.0+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 744.3 MB |
References
Sec 10K filings