Inconsistent Extraction of Table Headings Using Amazon Textract on the Same PDF
I'm using Amazon Textract to extract tabular data from PDFs, and I've observed inconsistent results when parsing the same document multiple times.
In particular, for the attached document, the table heading — “Consolidated Income from Property Operations” — is successfully extracted in one run but returns as None in another run.
This inconsistency is impacting the reliability and uniformity of the structured data we generate downstream.
❓ Has anyone else faced similar issues with header recognition inconsistency? 🔧 Configuration Details: FeatureTypes: ["TABLES"]
Document Type: Digital PDF (not scanned)
No preprocessing or layout changes between runs
💬 Specific Questions: Is there a way to ensure deterministic results for table extraction in Textract?
Are there configuration options (like seed, temperature, or post-processing controls) that can help improve consistency?
Are there best practices to reliably extract section/table headings like the one shown in the image?
Any guidance, suggestions, or workarounds to make the results more stable would be greatly appreciated
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The inconsistency you're experiencing with Amazon Textract's table heading extraction is a known limitation of the service. Textract can sometimes struggle with certain document layouts, particularly when it comes to identifying table titles and headers consistently.
This issue occurs because Textract's machine learning models may interpret the same document differently across multiple runs, especially when dealing with complex layouts or when the table heading is formatted in a way that makes it difficult to associate with the table itself.
To address this inconsistency, here are some approaches you can consider:
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Post-processing logic: Implement custom post-processing to handle cases where Textract fails to extract all the data consistently. This could involve looking for specific text patterns near tables that might represent headings.
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Use the LAYOUT feature: Instead of just using the "TABLES" feature, try including "LAYOUT" in your FeatureTypes. This can help identify section headers and titles more reliably, which you can then associate with tables based on their geometric positioning.
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Custom parsing: Consider implementing your own table parsing logic using the raw text extraction if the inconsistencies persist. This gives you more control over how the data is structured.
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Multiple extraction attempts: Process the document multiple times and compare results to find the most consistent output, though this is more of a workaround than a solution.
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Pre-processing: If possible, standardize your input PDFs before processing them with Textract to ensure more consistent results.
Unfortunately, there are no specific configuration options like seed or temperature controls in Textract that would help ensure deterministic results. The service doesn't currently offer parameters to control the consistency of extractions across multiple runs of the same document.
For your specific case with table headings, you might need to implement a custom solution that looks for text above tables within a certain proximity and identifies it as the potential table heading, especially if Textract fails to recognize it as such.
Sources
AWS Textract Missing Crucial Data in a Table | AWS re:Post
Textract Layout Response Not In Document Order | AWS re:Post
If AWS Textract itself makes a mistake by extracting the same table into different columns, what should be done? I want to merge all the tables to a single table in CSV. | AWS re:Post
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