Add Numeric Table Detection samples #48
There are no files selected for viewing
Binary file not shown.
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Binary file not shown.
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,343 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Numeric Table Detection\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "The objective is to demonstrate detection of tables in raw texxt to use targeted table extraction and lower the cost of using Textract.\n", | ||
| "Exmple for 1M pages per month containing 19% of pages with at leaqst 1 table. \n", | ||
| "\n", | ||
| "https://calculator.aws/#/estimate?id=fb01232c5a8b8304d1c1169b2911af087467ea82\n", | ||
| "\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Notebook Setup\n", | ||
| "\n", | ||
| "In this step, we will import some necessary libraries that will be used throughout this notebook. " | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": { | ||
| "scrolled": true, | ||
| "tags": [] | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "!python -m pip install -q amazon-textract-response-parser==0.1.44\n", | ||
| "!python -m pip install -q amazon-textract-caller==0.0.28\n", | ||
| "!python -m pip install -q amazon-textract-prettyprinter==0.0.16\n", | ||
| "!python -m pip install -q amazon-textract-textractor==1.1.1\n", | ||
| "!python -m pip install -q pypdf==3.1.0\n", | ||
| "!python -m pip install -q pandas==1.3.5" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": { | ||
| "tags": [] | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import boto3\n", | ||
| "import botocore\n", | ||
| "from IPython.display import Image, display, JSON, HTML\n", | ||
| "from textractcaller.t_call import call_textract, Textract_Features, call_textract_expense\n", | ||
| "from textractprettyprinter.t_pretty_print import convert_table_to_list\n", | ||
| "from trp import Document\n", | ||
| "import os\n", | ||
| "\n", | ||
| "\n", | ||
| "# variables\n", | ||
| "\n", | ||
| "s3=boto3.client('s3')\n", | ||
| "# because the reference document is stored in us-east-2, we run Textract in us-east-2 as well\n", | ||
| "textract = boto3.client('textract', region_name='us-east-2')\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": { | ||
| "tags": [] | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "file=\"./DemoTable.pdf\"\n", | ||
| "s3url=\"s3://amazon-textract-public-content/code-samples/DemoTable.pdf\"\n", | ||
| "!aws s3 cp s3://amazon-textract-public-content/code-samples/DemoTable.pdf .\n", | ||
| "!aws s3 cp s3://amazon-textract-public-content/code-samples/DemoTableDocument-ExtractedTablePages.pdf ." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": { | ||
| "tags": [] | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from IPython.display import IFrame\n", | ||
| "IFrame(file, width=1500, height=600)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "---\n", | ||
| "Extract unstructured data with Amazon Textract \n", | ||
| "\n", | ||
| "Amazon Textract is an ML powered OCR service that is capable of detecting and extracting text from documents. Text data in the form of WORDS and LINES can be extracted from documents using Amazon Textract `DetectDocumentText` API. Let's extract the words and lines from the demonstration document." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": { | ||
| "scrolled": true, | ||
| "tags": [] | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# Call Amazon Textract\n", | ||
| "response = call_textract(input_document=s3url, boto3_textract_client=textract)\n", | ||
| "\n", | ||
| "text=\"\"\n", | ||
| "# Print detected text\n", | ||
| "for item in response[\"Blocks\"]:\n", | ||
| " if item[\"BlockType\"] == \"LINE\":\n", | ||
| " text=text+\"\\n\"+item[\"Text\"]\n", | ||
| "print (text)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "---\n", | ||
| "# Detect Tables in raw text\n", | ||
| "\n", | ||
| "In this example using a threshold of 4 characters out of the 15 most used characters being digits works well to identify if the page is mostly constituted of numeric tables.\n", | ||
| "\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": { | ||
| "tags": [] | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from collections import Counter\n", | ||
| "import io\n", | ||
| "\n", | ||
| "def containsatable(textcontent):\n", | ||
| " counter = Counter(textcontent)\n", | ||
| " mostcom = counter.most_common(15)\n", | ||
| " countdigit=0\n", | ||
| " for i in mostcom :\n", | ||
| " if (i[0].isdigit()):\n", | ||
| " countdigit=countdigit+1\n", | ||
| " return(countdigit>=4)\n", | ||
| "\n", | ||
| "# Alternatively, we can identify if 50% of the lines satisfy the criteria (to identify tables embedded in text)\n", | ||
| "\n", | ||
| "def containsatableperline(textcontent):\n", | ||
| " countlines=0\n", | ||
| " tablelines=0\n", | ||
| " for line in io.StringIO(textcontent):\n", | ||
| " countlines=countlines+1\n", | ||
| " counter = Counter(line)\n", | ||
| " mostcom = counter.most_common(15)\n", | ||
| " countdigit=0\n", | ||
| " for i in mostcom :\n", | ||
| " if (i[0].isdigit()):\n", | ||
| " countdigit=countdigit+1\n", | ||
| " if (countdigit>=4):\n", | ||
| " tablelines=tablelines+1\n", | ||
| " \n", | ||
| " return(tablelines>(countlines/2))\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": { | ||
| "tags": [] | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from trp import Document\n", | ||
| "import math\n", | ||
| "doc = Document(response)\n", | ||
| "#print(doc.pages[0])\n", | ||
| "# Iterate over elements in the document\n", | ||
| "pagenum=0\n", | ||
| "pageswithtables=[]\n", | ||
| "pageswithtables2=[]\n", | ||
| "for page in doc.pages:\n", | ||
| " pagenum=pagenum+1\n", | ||
| " text=''\n", | ||
| " for line in page.lines:\n", | ||
| " text=text+line.text\n", | ||
| " if (containsatable(text)):\n", | ||
| " pageswithtables.append(pagenum)\n", | ||
| " if (containsatableperline(text)):\n", | ||
| " pageswithtables2.append(pagenum)\n", | ||
| "\n", | ||
| "print(\"List of pages with Tables:\") \n", | ||
| "print (pageswithtables)\n", | ||
| "print (pageswithtables2)\n", | ||
| "print(\"Percentage of pages with tables:\") \n", | ||
| "print (str(len(pageswithtables))+\"/\"+str(pagenum))\n", | ||
| "print (str(math.trunc(len(pageswithtables)/pagenum*100))+\"%\")\n", | ||
| " \n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Call Analyse Documents only on the pages with tables\n", | ||
| "First, we build a smaller PDF document containing only the pages identified as containing tables and then we run the AnalyzeDocument call on the new document." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": { | ||
| "tags": [] | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from pypdf import PdfReader, PdfWriter\n", | ||
| "\n", | ||
| "pdf_file_path = file\n", | ||
| "file_base_name = pdf_file_path.replace('.pdf', '')\n", | ||
| "\n", | ||
| "pdf = PdfReader(pdf_file_path)\n", | ||
| "\n", | ||
| "pages = pageswithtables\n", | ||
| "pdfWriter = PdfWriter()\n", | ||
| "\n", | ||
| "for page_num in pages:\n", | ||
| " pdfWriter.add_page(pdf.pages[page_num-1])\n", | ||
| "\n", | ||
| "pdf_out = open(\"temp.pdf\", 'wb')\n", | ||
| "pdfWriter.write(pdf_out)\n", | ||
| "pdf_out.close()\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
|
Comment on lines
+244
to
+247
There was a problem hiding this comment. add visualization for the newly created temp.pdf to show that it only contains tables |
||
| "execution_count": null, | ||
| "metadata": { | ||
| "tags": [] | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from IPython.display import IFrame\n", | ||
| "IFrame('temp.pdf', width=1500, height=600)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": { | ||
| "tags": [] | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# When you are using your own document, you need to copy the result to S3 and process that.\n", | ||
| "# In our sample case, we already uploaded the output to S3, so the next line is commented out\n", | ||
| "#!aws s3 cp temp.pdf s3://{data_bucket}/temp.pdf --only-show-errors\n", | ||
| "s3url=\"s3://amazon-textract-public-content/code-samples/DemoTableDocument-ExtractedTablePages.pdf\"" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": { | ||
| "tags": [] | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import pandas as pd\n", | ||
| "resp = call_textract(input_document=s3url, features=[Textract_Features.TABLES])\n", | ||
| "tdoc = Document(resp)\n", | ||
| "dfs = list()\n", | ||
| "\n", | ||
| "for page in tdoc.pages:\n", | ||
| " for table in page.tables:\n", | ||
| " tab_list = convert_table_to_list(trp_table=table)\n", | ||
| " print(tab_list)\n", | ||
| " dfs.append(pd.DataFrame(tab_list))\n", | ||
| "\n", | ||
| "df1 = dfs[0]\n", | ||
| "df2 = dfs[1]" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "df1" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "df2" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "instance_type": "ml.t3.medium", | ||
| "kernelspec": { | ||
| "display_name": "Python 3 (Data Science)", | ||
| "language": "python", | ||
| "name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-east-1:081325390199:image/datascience-1.0" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.7.10" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 4 | ||
| } | ||
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
explain in more detail how this implementation works. This is the key point of the notebook/sample, so we want to be very clear about this. Provide a second implementation as well, so the concept is understood.
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
Added a second implementation focusing on each line.