Amazon Textract

Where Amazon Textract falls short, per the models

• GPT Its verbose block graph demands substantial reconstruction logic, and irregular cross-page tables remain troublesome
• Claude Noticeably weaker than newer VLM-based parsers on complex layouts — merged cells, rotated pages, and multi-page table continuity often need post-processing glue code.
• Gemini Struggles to extract highly irregular, non-standard, or nested financial tables compared to vision-language model alternatives, and its nested JSON response is notoriously painful to parse.
• Grok Not ideal for non-AWS environments or extreme customization without additional dev effort; pricing can add up for massive volumes.

Poll history — On this board 2 of 2 polls since Jul 18 · now #1

#2 → #1

Where Amazon Textract falls short, per the models

• GPT Handwriting recognition is English-only and is materially less reliable on cursive than on neat handprint.
• Claude Handwriting accuracy trails Google/Azure on difficult cursive; non-English handwriting support is comparatively limited.
• Gemini Struggles with unstructured multi-page handwritten prose, heavily cursive non-Latin scripts, or low-resolution scans lacking clear visual field delimiters.

Poll history — #3 in all 2 polls since Aug 4

#3 → #3

Where Amazon Textract falls short, per the models

• GPT Advanced structure features become expensive, and general multilingual and complex-layout performance is less compelling than the leaders.
• Claude Weakest language coverage of the big three (heavily English/Latin-script biased) and per-feature pricing (tables + forms + queries stack) makes rich extraction notably pricier than plain text.
• Gemini Substantially weaker layout parsing and text flow preservation for multi-column documents compared to Azure and LlamaParse, making it unsuitable for feeding text directly into LLM pipelines where reading order matters.
• Grok Weaker than Azure on very complex or irregular layouts and line-item parsing in some tests; less flexible custom training.

Poll history — On this board 2 of 2 polls since Jul 18 · now #2

#3 → #2

Where Amazon Textract falls short, per the models

• GPT Its fixed invoice schema and limited adaptation tools often require more validation and post-processing on unusual supplier formats.
• Claude Weaker non-English/non-Latin coverage than Azure and no custom model training for expense documents — if your invoices are German, Japanese, or heavily non-standard, you'll be post-processing a lot.
• Grok Requires additional post-processing/normalization for full structured output; best for AWS users, less turnkey for non-cloud teams

Poll history — On this board 2 of 2 polls since Jul 17 · now #1

#3 → #1

Where Amazon Textract falls short, per the models

• GPT AWS itself documents inconsistent results on irregular and merged-cell tables, so it is strongest for high-volume conventional business documents rather than the hardest visually complex PDFs. ([AWS Documentation][7])
• Claude Struggles more than Azure on deeply nested/spanning headers and dense financial tables, and the raw block-graph output requires meaningful post-processing to reassemble clean tables.
• Gemini Outputs rigid, low-level JSON blocks that demand heavy post-processing to reconstruct semantic context, and falls behind vision-LLM parsers on stylized borderless tables.

Poll history — On this board 2 of 2 polls since Aug 4 · now #5

#3 → #5

Where Amazon Textract falls short, per the models

• GPT Raw output needs substantial normalization and business-rule engineering, especially for complex tables and production-grade AP matching
• Claude Line-item and tax extraction on dense or non-US-style invoices lags Azure and specialists, so expect more downstream correction
• Grok Accuracy lags specialized tools on highly variable or messy layouts (around 78-82% in benchmarks); requires additional custom code for full AP validation/workflows.

Poll history — On this board 2 of 2 polls since Jul 18 · now #4

#6 → #4

Where Amazon Textract falls short, per the models

• Grok Less emphasis on semantic reconstruction/LLM-ready Markdown compared to newer VLM tools (not for rapid prototyping or non-AWS environments).