AWS Textract responds with different number of tables in Console and Boto3 1.38.43
Hello,
I am getting a peculiar variation in outputs when using the Textract service in the console compared to in Python using Boto3. The image of the page is attached.
In the Textract console I get a single table as the output while using Textract Client in Boto3 (1.38.43) with Python 3.10.12 I get two tables - first with the top two rows of table and the second with the rest of the table.
I am converting the page to bytes using these two methods:-
Method 1:
page = pdf_doc[page_num] #Fitz PDF object zoom = 3 mat = fitz.Matrix(zoom, zoom) pix = page.get_pixmap(matrix=mat, alpha=False) img_bytes = pix.tobytes() page_bw_bytes = remove_gray_patches_cv2(img_bytes, threshold=200) #Function to remove gray patches
Method 2:
page = pdf_doc[page_num] #Fitz PDF object single_page_doc = fitz.open() single_page_doc.insert_pdf(pdf_doc, from_page=page_num, to_page=page_num) page_pdf_bytes = single_page_doc.tobytes()
I have tried both synchronous (analyze_document) and asynchronous (start_document_analysis) with the two methods given above and still get two tables using the Boto3 Textract client.
Any help would be appreciated.
Thanks
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The discrepancy you're seeing between the AWS Textract console and Boto3 results is likely due to how Textract processes and interprets table structures in documents.
This type of inconsistency can occur for several reasons:
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Table complexity: Textract may have difficulty interpreting complex table structures consistently. If your table has merged cells, unusual formatting, or lacks clear borders, Textract might interpret it differently across different processing methods.
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Image quality and preprocessing: The console and your code may be handling the document differently. The preprocessing you're doing (like applying zoom, converting to bytes, and removing gray patches) could be affecting how Textract interprets the table boundaries.
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Resolution differences: The zoom factor of 3 you're applying in Method 1 changes the resolution of the image, which might cause Textract to interpret table structures differently than the console does.
To address this issue, you could try:
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Adjust image quality: Try different zoom levels or preprocessing techniques to see if you can get more consistent results.
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Use PDF directly: When possible, submit the PDF directly to Textract rather than converting to images first, as Textract can often handle PDFs more effectively.
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Post-processing: Implement logic to merge split tables based on their proximity or structure if this is a recurring issue.
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Check API versions: Ensure you're using the latest version of the Textract API, as improvements are regularly made to the service.
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Try the helper libraries: Consider using open-source helper libraries like amazon-textract-textractor (Python) or amazon-textract-response-parser (JavaScript/TypeScript) which can simplify working with Textract responses.
This type of inconsistency is a known challenge with document analysis services like Textract, especially when dealing with complex table structures. The service is continuously improving, but some manual post-processing may still be necessary for certain documents.
Sources
AWS Textract Missing Crucial Data in a Table | AWS re:Post
How to extract page number from Amazon Textract | AWS re:Post
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