PDF DataSource for Apache Spark in Python
Project description
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Source Code: https://github.com/StabRise/spark-pdf
Quick Start Jupyter Notebook Spark 3.5.x on Databricks: PdfDataSourceSpark4.ipynb
Quick Start Jupyter Notebook Spark 3.x.x: PdfDataSource.ipynb
Quick Start Jupyter Notebook Spark 4.0.x: PdfDataSourceSpark4.ipynb
Welcome to the Spark PDF
The project provides a custom data source for the Apache Spark that allows you to read PDF files into the Spark DataFrame.
If you found useful this project, please give a star to the repository.
👉 Works on Databricks now. See the Databricks example.
Key features:
- Read PDF documents to the Spark DataFrame
- Support efficient read PDF files lazy per page
- Support big files, up to 10k pages
- Support scanned PDF files (call OCR for text recognition from the images)
- No need to install Tesseract OCR, it's included in the package
- 👉 Compatible with ScaleDP, an Open-Source Library for Processing Documents using AI/ML in Apache Spark.
Requirements
- Java 8, 11, 17
- Apache Spark 3.3.2, 3.4.1, 3.5.0, 4.0.0
- Ghostscript 9.50 or later (only for the GhostScript reader)
Spark 4.0.0 is supported in the version 0.1.11 and later (need Java 17 and Scala 2.13).
Installation
Binary package is available in the Maven Central Repository.
- Spark 3.5.*: com.stabrise:spark-pdf-spark35_2.12:0.1.14
- Spark 3.4.*: com.stabrise:spark-pdf-spark34_2.12:0.1.11
- Spark 3.3.*: com.stabrise:spark-pdf-spark33_2.12:0.1.11
- Spark 4.0.*: com.stabrise:spark-pdf-spark34_2.13:0.1.11
Options for the data source:
imageType: Oputput image type. Can be: "BINARY", "GREY", "RGB". Default: "RGB".resolution: Resolution for rendering PDF page to the image. Default: "300" dpi.pagePerPartition: Number pages per partition in Spark DataFrame. Default: "5".reader: Supports:pdfBox- based on PdfBox java lib,gs- based on GhostScript (need installation GhostScipt to the system)ocrConfig: Tesseract OCR configuration. Default: "psm=3". For more information see Tesseract OCR Params
Output Columns in the DataFrame:
The DataFrame contains the following columns:
path: path to the filepage_number: page number of the documenttext: extracted text from the text layer of the PDF pageimage: image representation of the pagedocument: the OCR-extracted text from the rendered image (calls Tesseract OCR)partition_number: partition number
Output Schema:
root
|-- path: string (nullable = true)
|-- filename: string (nullable = true)
|-- page_number: integer (nullable = true)
|-- partition_number: integer (nullable = true)
|-- text: string (nullable = true)
|-- image: struct (nullable = true)
| |-- path: string (nullable = true)
| |-- resolution: integer (nullable = true)
| |-- data: binary (nullable = true)
| |-- imageType: string (nullable = true)
| |-- exception: string (nullable = true)
| |-- height: integer (nullable = true)
| |-- width: integer (nullable = true)
|-- document: struct (nullable = true)
| |-- path: string (nullable = true)
| |-- text: string (nullable = true)
| |-- outputType: string (nullable = true)
| |-- bBoxes: array (nullable = true)
| | |-- element: struct (containsNull = true)
| | | |-- text: string (nullable = true)
| | | |-- score: float (nullable = true)
| | | |-- x: integer (nullable = true)
| | | |-- y: integer (nullable = true)
| | | |-- width: integer (nullable = true)
| | | |-- height: integer (nullable = true)
| |-- exception: string (nullable = true)
Example of usage
Scala
import org.apache.spark.sql.SparkSession
val spark = SparkSession.builder()
.appName("Spark PDF Example")
.master("local[*]")
.config("spark.jars.packages", "com.stabrise:spark-pdf-spark35_2.12:0.1.11")
.getOrCreate()
val df = spark.read.format("pdf")
.option("imageType", "BINARY")
.option("resolution", "200")
.option("pagePerPartition", "2")
.option("reader", "pdfBox")
.option("ocrConfig", "psm=11")
.load("path to the pdf file(s)")
df.select("path", "document").show()
Python
from pyspark.sql import SparkSession
spark = SparkSession.builder \
.master("local[*]") \
.appName("SparkPdf") \
.config("spark.jars.packages", "com.stabrise:spark-pdf-python-spark35_2.12:0.1.11") \
.getOrCreate()
df = spark.read.format("pdf") \
.option("imageType", "BINARY") \
.option("resolution", "200") \
.option("pagePerPartition", "2") \
.option("reader", "pdfBox") \
.option("ocrConfig", "psm=11") \
.load("path to the pdf file(s)")
df.select("path", "document").show()
Disclaimer
This project is not affiliated with, endorsed by, or connected to the Apache Software Foundation or Apache Spark.
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