DeepSeek PDF Uploading: PDF Reading Capabilities, Text Extraction Accuracy, Layout Support, And File Limitations
- Michele Stefanelli
- 17 hours ago
- 3 min read
DeepSeek offers PDF uploading and document analysis as part of its consumer app experience, along with specialized OCR capabilities for scanned documents and structured layout extraction. Understanding its PDF reading features, extraction quality, and practical limitations helps users maximize value in both text-centric and image-heavy workflows.
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PDF Reading In DeepSeek Combines File Upload With Text Extraction And OCR For Scanned Content.
DeepSeek’s consumer app supports direct file upload and text extraction from PDFs, enabling users to query, summarize, or analyze document content. Uploaded PDFs with embedded, selectable text are processed for fast and accurate extraction, delivering clean text for downstream language model operations.
For developer and API workflows, DeepSeek recommends extracting text or table content from PDFs externally before sending excerpts into the model, as the primary API is designed for chat completions with text messages, not direct PDF file ingestion.
The DeepSeek-OCR model family extends document capabilities to image-based and scanned PDFs, decoding visual content and supporting multilingual recognition.
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DeepSeek PDF Reading Capabilities
Feature | Description |
Consumer app file upload | Direct PDF upload and extraction |
API usage | Requires external text extraction |
OCR for scanned PDFs | DeepSeek-OCR model line |
Multilingual support | OCR handles multiple languages |
Text-based and scanned PDFs are both supported with appropriate workflow.
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Text Extraction Accuracy Is High For Digital PDFs And Competitive For Scanned Images.
DeepSeek achieves strong extraction accuracy for digital PDFs containing embedded text, with output closely matching original document content. For scanned or image-based PDFs, DeepSeek-OCR delivers robust optical character recognition, reporting up to 97% accuracy for clear, low-compression scans, and reduced accuracy as image compression increases.
Extraction quality may vary based on PDF structure, language, compression level, and the presence of tables or complex layouts. High-quality, well-formed PDFs yield the most reliable text output.
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DeepSeek Text Extraction And OCR Accuracy
Document Type | Extraction Approach | Reported Accuracy |
Digital PDF (selectable text) | Native extraction | High, near-original |
Scanned PDF (images) | DeepSeek-OCR | 97% (low compression), ~60% (high compression) |
Document quality and scan settings directly impact extraction results.
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Layout Support Includes Table Structure, Multilingual Recognition, And Output Flexibility.
DeepSeek-OCR is designed to preserve and reconstruct PDF layout, supporting both structured table outputs and layout-agnostic extraction modes. The model can decode tables, lists, and multi-column text, and supports non-English documents.
Layout-aware outputs are particularly useful for business, legal, and academic documents where table structure and text arrangement matter for downstream analysis.
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DeepSeek PDF Layout And Structure Handling
Capability | Support Details |
Table extraction | Yes, with structured output |
Multilingual OCR | Supported |
Layout preservation | Layout and non-layout formats available |
Complex formatting | Supported, subject to OCR token budget |
Layout support increases document analysis accuracy for structured content.
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File Limitations Are Unpublished For The Consumer App And Context-Driven For API Use.
DeepSeek’s consumer-facing documentation does not specify hard caps on PDF file size, page count, or the number of uploaded documents per session. Users may encounter practical upload limits based on application performance, file type, or network constraints.
For developer and API use, the effective limit is determined by the model’s context window and input length constraints. Extracted PDF text must fit within these bounds, making long documents subject to chunking or summarization before submission.
Scanned or image-heavy PDFs require more compute resources for OCR and may be slower or more costly to process.
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DeepSeek PDF File Limitations
Workflow | Published Limit | Practical Consideration |
Consumer app upload | Not published | Subject to app and network constraints |
API workflow | Context window limited | Input text length governs capacity |
OCR processing | No public size cap | Large scans may increase latency, reduce accuracy |
Planning for file size and structure optimizes performance and accuracy.
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DeepSeek PDF Uploading Supports Both Text And Image-Based Documents With Flexible, High-Accuracy Extraction.
DeepSeek enables robust PDF reading, combining file upload and native text extraction for digital documents, and advanced OCR for scanned images and complex layouts. While hard file limits are not published, practical usage is governed by document structure, API context limits, and the quality of uploaded content.
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