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.


You can download the source code from

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

    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 = 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 file

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