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    A two-pass approach to OCR with LiteParse and LlamaParse

    LlamaIndex reports a 32-second first pass through a full data room with LiteParse, a free, open-source parser supporting 50-plus formats.

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    TLDR

    LlamaIndex describes a two-pass workflow for optical character recognition (OCR): LiteParse extracts text and layout details and flags page complexity, then LlamaParse focuses on pages that need more processing, returning cell-level tables, bounding boxes and confidence scores. A post sharing the breakdown says the approach works well for small to medium batches, such as 10–100 documents, but comes with cost and latency trade-offs and is not a substitute for large-scale offline indexing and retrieval.

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    3 Sources, first seen 16d ago

    Combined views

    35.5K

    3 Sources, first seen 16d ago

    186 likes
    16d ago
    first seen 16d ago
    186 likes
    40 comments
    204 saves
    25 reposts

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    40 comments
    204 saves
    25 reposts

    Sentiment

    Positive——Negative

    Summary

    Not enough discussion yet.

    No sentiment analysis available yet.

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    3 Sources

    @llama_indexjust-in-time OCR is all the rage. most pipelines parse every page before anyone asks a question. for an agent working through an ad-hoc data room, that's slow, expensive, and most of those pages never get read. the better pattern is just-in-time OCR in two passes: ✅️ LiteParse (free, OSS, Rust, 50+ formats) does a fast layout-aware first pass: spatial text, bounding boxes, headings, tables, and a per-page complexity flag. a full data room in 32 seconds. ✅️ LlamaParse zooms in on only the pages that need it, by page number, and returns cell-level tables, bounding boxes, and confidence scores. the rest fills in the background. pypdf and pdftotext can't do the first pass well. parsing everything up front can't do it cheaply. two passes gets you both. full breakdown with numbers: https://www.llamaindex.ai/blog/just-in-time-agentic-ocr
    @jerryjliu0RT @llama_index: just-in-time OCR is all the rage. most pipelines parse every page before anyone asks a question. for an agent working thr…

    3 Sources

    @llama_indexjust-in-time OCR is all the rage. most pipelines parse every page before anyone asks a question. for an agent working through an ad-hoc data room, that's slow, expensive, and most of those pages never get read. the better pattern is just-in-time OCR in two passes: ✅️ LiteParse (free, OSS, Rust, 50+ formats) does a fast layout-aware first pass: spatial text, bounding boxes, headings, tables, and a per-page complexity flag. a full data room in 32 seconds. ✅️ LlamaParse zooms in on only the pages that need it, by page number, and returns cell-level tables, bounding boxes, and confidence scores. the rest fills in the background. pypdf and pdftotext can't do the first pass well. parsing everything up front can't do it cheaply. two passes gets you both. full breakdown with numbers: https://www.llamaindex.ai/blog/just-in-time-agentic-ocr
    @jerryjliu0RT @llama_index: just-in-time OCR is all the rage. most pipelines parse every page before anyone asks a question. for an agent working thr…