{"type":"post","id":"only-18-mb-of-onnx-weights-no-pytorch-61535a","slug":"only-18-mb-of-onnx-weights-no-pytorch-61535a","title":"Only 18 MB of ONNX weights, no PyTorch; weights on Hugging Face .","description":"Indra OCR makes local document extraction easier to deploy by packaging OCR weights and a usable interface in a small ONNX Runtime distribution.","retrieval_nugget":"Indra OCR makes local document extraction easier to deploy by packaging OCR weights and a usable interface in a small ONNX Runtime distribution.","published_at":"2026-10-17","updated_at":"2026-10-17","record_date":"2026-10-17","date_kind":"published_at","topics":["local-models","models"],"source_urls":["https://huggingface.co/datalab-to/indra-ocr"],"schema_version":"newruntime-agent-readable-v0.2","stable_id":"post:only-18-mb-of-onnx-weights-no-pytorch-61535a","status":"published","visuals":[],"editorial_provenance":{"schema_version":"newruntime-editorial-copy-v1","content_status":"source_grounded_final","final_copy_sha256":"sha256:1cdf630924107ee828cd60d57f37e8655adc88aac199c27f7e9e61b07cc649af","reviewed_at":"2026-10-17T14:03:04.949Z","source_evidence_count":1,"verified_claim_count":4,"site_analysis_schema_version":"newruntime-site-analysis-v1","site_object_kind":"field_note","observed_fact_count":4,"implication_count":1,"watch_condition_count":1,"related_record_count":0},"analysis":{"schema_version":"newruntime-site-analysis-v1","object_kind":"field_note","thesis":"Indra OCR makes local document extraction easier to deploy by packaging OCR weights and a usable interface in a small ONNX Runtime distribution.","observed_facts":[{"text":"The model card describes about 18 MB of model files and says installation does not require PyTorch.","source_urls":["https://huggingface.co/datalab-to/indra-ocr"]},{"text":"The project provides CLI and Python entry points for PDFs and images, with Markdown, text, or JSON outputs that can include line boxes and labeled layout blocks.","source_urls":["https://huggingface.co/datalab-to/indra-ocr"]},{"text":"The model card reports up to 49 pages per second on CPU and a score of 41.3 on OmniParseBench Text.","source_urls":["https://huggingface.co/datalab-to/indra-ocr"]},{"text":"The card identifies Apache 2.0 for code and a modified OpenRAIL-M license for weights, with commercial-use restrictions described by user category.","source_urls":["https://huggingface.co/datalab-to/indra-ocr"]}],"mechanism":"The mechanism is a compact runtime package paired with outputs that preserve structure: Markdown, text, or JSON with line boxes and labeled layout blocks.","why_now":"Why now: a small CPU-oriented package gives document-processing teams another route to test OCR without bringing a PyTorch installation into the deployment.","implications":["The first implication is that teams can evaluate structured, locally processed OCR outputs with a lightweight setup rather than treating OCR as a hosted-only service."],"evidence_boundary":"The evidence boundary is the project’s model card: it documents interfaces, stated sizes, benchmark results, and licensing terms, but this evidence does not independently establish comparative performance or suitability for a given workload.","watch_conditions":["Watch whether independent tests on representative scans confirm the claimed speed and accuracy, and whether the weight-license terms fit intended commercial use."],"related_records":[],"new_branch_reason":"The release is distinct from earlier OCR records because this source describes a small local ONNX package and its deployment and licensing details, rather than a long-document parsing approach."},"editor":{"role":"editor","name":"Andrey Reshetnikov","profile":"https://newruntime.com/owner-profile.md","profile_json":"https://newruntime.com/owner-profile.json","same_as":["https://www.linkedin.com/in/reshetnikov1/","https://t.me/lapetuse"]},"routes":{"html":"https://newruntime.com/posts/only-18-mb-of-onnx-weights-no-pytorch-61535a/","markdown":"https://newruntime.com/posts/only-18-mb-of-onnx-weights-no-pytorch-61535a.md","json":"https://newruntime.com/posts/only-18-mb-of-onnx-weights-no-pytorch-61535a.json"},"related_shifts":["document-processing-shifts-from-hand-crafted-ocr-pipelines-to-llm"],"next_reads":[{"type":"topic","path":"/topics/local-models/","url":"https://newruntime.com/topics/local-models/","title":"Local Models","reason":"Explore the local-models topic 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