Amazon Textract
Developer Guide

Exporting Tables into a CSV File

This Python example shows how to export tables into a comma-separated values (CSV) file. Table information is returned as Block objects from a call to AnalyzeDocument. For more information, see Tables. The Block objects are stored in a map structure that's used to export the table data into a CSV file.

The functions that are specific to Amazon Textract are:

  • get_table_csv_results – Calls AnalyzeDocument, and builds a map of tables that are detected in the document. Creates a CSV representation of all detected tables.

  • generate_table_csv – Generates the CSV file for an individual table.

  • get_rows_columns_map – Gets the rows and columns from the map.

  • get_text – Gets the text from a cell.

Note

You can download the source code from textract_python_table_parser.py.

To export tables into a CSV file

  1. Configure your environment. For more information, see Prerequisites.

  2. Save the following example code to a file named textract_python_table_parser.py.

    import webbrowser, os import json import boto3 import io from io import BytesIO import sys from pprint import pprint def get_rows_columns_map(table_result, blocks_map): rows = {} for relationship in table_result['Relationships']: if relationship['Type'] == 'CHILD': for child_id in relationship['Ids']: cell = blocks_map[child_id] if cell['BlockType'] == 'CELL': row_index = cell['RowIndex'] col_index = cell['ColumnIndex'] if row_index not in rows: # create new row rows[row_index] = {} # get the text value rows[row_index][col_index] = get_text(cell, blocks_map) return rows def get_text(result, blocks_map): text = '' if 'Relationships' in result: for relationship in result['Relationships']: if relationship['Type'] == 'CHILD': for child_id in relationship['Ids']: word = blocks_map[child_id] if word['BlockType'] == 'WORD': text += word['Text'] + ' ' if word['BlockType'] == 'SELECTION_ELEMENT': if word['SelectionStatus'] =='SELECTED': text += 'X ' return text def get_table_csv_results(file_name): with open(file_name, 'rb') as file: img_test = file.read() bytes_test = bytearray(img_test) print('Image loaded', file_name) # process using image bytes # get the results client = boto3.client('textract') response = client.analyze_document(Document={'Bytes': bytes_test}, FeatureTypes=['TABLES']) # Get the text blocks blocks=response['Blocks'] pprint(blocks) blocks_map = {} table_blocks = [] for block in blocks: blocks_map[block['Id']] = block if block['BlockType'] == "TABLE": table_blocks.append(block) if len(table_blocks) <= 0: return "<b> NO Table FOUND </b>" csv = '' for index, table in enumerate(table_blocks): csv += generate_table_csv(table, blocks_map, index +1) csv += '\n\n' return csv def generate_table_csv(table_result, blocks_map, table_index): rows = get_rows_columns_map(table_result, blocks_map) table_id = 'Table_' + str(table_index) # get cells. csv = 'Table: {0}\n\n'.format(table_id) for row_index, cols in rows.items(): for col_index, text in cols.items(): csv += '{}'.format(text) + "," csv += '\n' csv += '\n\n\n' return csv def main(file_name): table_csv = get_table_csv_results(file_name) output_file = 'output.csv' # replace content with open(output_file, "wt") as fout: fout.write(table_csv) # show the results print('CSV OUTPUT FILE: ', output_file) if __name__ == "__main__": file_name = sys.argv[1] main(file_name)
  3. At the command prompt, enter the following command. Replace file with the document image file that you want to analyze.

    python textract_python_table_parser.py file

When you run the example, the CSV output is saved to a file named output.csv.