An Open-Source Library for Processing Documents using AI/ML in Apache Spark.

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Source Code: https://github.com/StabRise/ScaleDP

Quickstart: 1.QuickStart.ipynb

Documentations: https://scaledp.stabrise.com

Tutorials: https://github.com/StabRise/ScaleDP-Tutorials

Change Log

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Welcome to the ScaleDP library

ScaleDP is library allows you to process documents using AI/ML capabilities and scale it using Apache Spark.

LLM (Large Language Models) and VLM (Vision Language Models) models are used to extract data from text and images in combination with OCR engines.

Discover pre-trained models for your projects or play with the thousands of models hosted on the Hugging Face Hub.

Key features

Document processing:

• ✅ Loading PDF documents/Images to the Spark DataFrame (using Spark PDF Datasource and as binaryFile)
• ✅ Extraction text/images from PDF documents/Images
• ✅ Zero-Shot extraction structured data from text/images using LLM and ML models
• ✅ Possibility run as REST API service without Spark Session for have minimum processing latency
• ✅ Support Streaming mode for processing documents in real-time

LLM:

Support OpenAI compatible API for call LLM/VLM models (GPT, Gemini, GROQ, etc.)

• OCR Images/PDF documents using Vision LLM models
• Extract data from the image using Vision LLM models
• Extract data from the text/images using LLM models
• Extract data using DSPy framework
• NER using LLM's
• Visualize results

NLP:

• Extract data from the text/images using NLP models from the Hugging Face Hub
• NER using classical ML models

OCR:

Support various open-source OCR engines:

• Tesseract OCR
• Easy OCR
• Surya OCR
• DocTR
• Vision LLM models

CV:

• Object detection on images using YOLO models
• Text detection on images

Installation

Prerequisites

• Python 3.10 or higher
• Apache Spark 3.5 or higher
• Java 8

Installation using pip

Install the ScaleDP package with pip:

pip install scaledp

Installation using Docker

Build image:

 docker build -t scaledp .

Run container:

 docker run -p 8888:8888 scaledp:latest

Open Jupyter Notebook in your browser:

 http://localhost:8888

Qiuckstart

Start a Spark session with ScaleDP:

fromscaledpimport*spark=ScaleDPSession()
spark

Read example image file:

image_example=files('resources/images/Invoice.png')
df=spark.read.format("binaryFile") \
.load(image_example)
df.show_image("content")

Output:

Zero-Shot data Extraction from the Image:

frompydanticimportBaseModelimportjsonclassItems(BaseModel):
date: stritem: strnote: strdebit: strclassInvoiceSchema(BaseModel):
hospital: strtax_id: straddress: stremail: strphone: stritems: list[Items]
total: strpipeline=PipelineModel(stages=[
DataToImage(
inputCol="content",
outputCol="image"
),
LLMVisualExtractor(
inputCol="image",
outputCol="invoice",
model="gemini-1.5-flash",
apiKey="",
apiBase="https://generativelanguage.googleapis.com/v1beta/",
schema=json.dumps(InvoiceSchema.model_json_schema())
)
])
result=pipeline.transform(df).cache()

Show the extracted json:

result.show_json("invoice")

Let's show Invoice as Structured Data in Data Frame

result.select("invoice.data.*").show()

Output:

+-------------------+---------+--------------------+--------------------+--------------+--------------------+-------+
| hospital| tax_id| address| email| phone| items| total|
+-------------------+---------+--------------------+--------------------+--------------+--------------------+-------+
|Hope Haven Hospital|26-123123|855 Howard Street...|hopedutton@hopeha...|(123) 456-1238|[{10/21/2022, App...|1024.50|
+-------------------+---------+--------------------+--------------------+--------------+--------------------+-------+

Schema:

result.printSchema()

root
|-- path: string (nullable = true)
|-- modificationTime: timestamp (nullable = true)
|-- length: long (nullable = true)
|-- image: struct (nullable = true)
| |-- path: string (nullable = false)
| |-- resolution: integer (nullable = false)
| |-- data: binary (nullable = false)
| |-- imageType: string (nullable = false)
| |-- exception: string (nullable = false)
| |-- height: integer (nullable = false)
| |-- width: integer (nullable = false)
|-- invoice: struct (nullable = true)
| |-- path: string (nullable = false)
| |-- json_data: string (nullable = true)
| |-- type: string (nullable = false)
| |-- exception: string (nullable = false)
| |-- processing_time: double (nullable = false)
| |-- data: struct (nullable = true)
| | |-- hospital: string (nullable = false)
| | |-- tax_id: string (nullable = false)
| | |-- address: string (nullable = false)
| | |-- email: string (nullable = false)
| | |-- phone: string (nullable = false)
| | |-- items: array (nullable = false)
| | | |-- element: struct (containsNull = false)
| | | | |-- date: string (nullable = false)
| | | | |-- item: string (nullable = false)
| | | | |-- note: string (nullable = false)
| | | | |-- debit: string (nullable = false)
| | |-- total: string (nullable = false)

NER using model from the HuggingFace models Hub

Define pipeline for extract text from the image and run NER:

pipeline=PipelineModel(stages=[
DataToImage(inputCol="content", outputCol="image"),
TesseractOcr(inputCol="image", outputCol="text", psm=PSM.AUTO, keepInputData=True),
Ner(model="obi/deid_bert_i2b2", inputCol="text", outputCol="ner", keepInputData=True),
ImageDrawBoxes(inputCols=["image", "ner"], outputCol="image_with_boxes", lineWidth=3, padding=5, displayDataList=['entity_group'])
])
result=pipeline.transform(df).cache()
result.show_text("text")

Output:

Show NER results:

result.show_ner(limit=20)

Output:

+------------+-------------------+----------+-----+---+--------------------+
|entity_group| score| word|start|end| boxes|
+------------+-------------------+----------+-----+---+--------------------+
| HOSP| 0.991257905960083| Hospital| 0| 8|[{Hospital:, 0.94...|
| LOC| 0.999171257019043| Dutton| 10| 16|[{Dutton,, 0.9609...|
| LOC| 0.9992585778236389| MI| 18| 20|[{MI, 0.93335297,...|
| ID| 0.6838774085044861| 26| 29| 31|[{26-123123, 0.90...|
| PHONE| 0.4669836759567261| -| 31| 32|[{26-123123, 0.90...|
| PHONE| 0.7790696024894714| 123123| 32| 38|[{26-123123, 0.90...|
| HOSP|0.37445762753486633| HOPE| 39| 43|[{HOPE, 0.9525460...|
| HOSP| 0.9503226280212402| HAVEN| 44| 49|[{HAVEN, 0.952546...|
| LOC| 0.9975488185882568|855 Howard| 59| 69|[{855, 0.94682700...|
| LOC| 0.9984399676322937| Street| 70| 76|[{Street, 0.95823...|
| HOSP| 0.3670221269130707| HOSPITAL| 77| 85|[{HOSPITAL, 0.959...|
| LOC| 0.9990363121032715| Dutton| 86| 92|[{Dutton,, 0.9647...|
| LOC| 0.999313473701477| MI 49316| 94|102|[{MI, 0.94589012,...|
| PHONE| 0.9830010533332825| ( 123 )| 110|115|[{(123), 0.595334...|
| PHONE| 0.9080978035926819| 456| 116|119|[{456-1238, 0.955...|
| PHONE| 0.9378324151039124| -| 119|120|[{456-1238, 0.955...|
| PHONE| 0.8746233582496643| 1238| 120|124|[{456-1238, 0.955...|
| PATIENT|0.45354968309402466|hopedutton| 132|142|[{hopedutton@hope...|
| EMAIL|0.17805588245391846| hopehaven| 143|152|[{hopedutton@hope...|
| HOSP| 0.505658745765686| INVOICE| 157|164|[{INVOICE, 0.9661...|
+------------+-------------------+----------+-----+---+--------------------+

Visualize NER results:

result.visualize_ner(labels_list=["DATE", "LOC"])

Original image with NER results:

result.show_image("image_with_boxes")

Ocr engines

	Bbox level	Support GPU	Separate model for text detection	Processing time 1 page (CPU/GPU) secs	Support Handwritten Text
Tesseract OCR	character	no	no	0.2/no	not good
Tesseract OCR CLI	character	no	no	0.2/no	not good
Easy OCR	word	yes	yes
Surya OCR	line	yes	yes
DocTR	word	yes	yes

Projects used the ScaleDP

• PDF Redaction - Free AI-powered tool for redact PDF files (remove sensitive information) online.
• frontiersin.org - Reviewer Recommendation System for academic articles using ScaleDP and LLM models.
• dayme.tech - Data extraction from scanned receipts using ScaleDP and LLM models.
• curie.is - Curie used ScaleDP for document preprocessing and analysis data in documents.

Disclaimer

This project is not affiliated with, endorsed by, or connected to the Apache Software Foundation or Apache Spark.