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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →TeleOCR is a roughly 1.2-billion-parameter vision-language model designed to turn digital documents and camera-captured pages into structured, machine-readable output. Its distinctive focus is geometric distortion: the project describes a layout-first approach that represents warped pages with polygons and aims to recognize their contents without requiring a separate page-rectification model. The TeleOCR team reports strong benchmark scores, but those are project evaluations—not independent confirmation of a general performance lead.
What TeleOCR is designed to do
Document parsing goes beyond recognizing characters: it turns a page into usable structures such as text, tables, formulas, and layout regions. Cai et al. describe the goal as transforming “unstructured documents into structured and machine-readable representations” in the paper abstract.
TeleOCR is presented as a compact vision-language model for that task, with support intended for both clean digital documents and pages captured by camera, including geometrically distorted pages. The model card lists text, table, formula, code, layout, distorted-page polygon layout, and scientific-chart-to-table tasks. Tasks are selected through prompts; the card points users to the project’s parsing repository for complete document parsing.
How its approach handles page structure
The paper frames two challenges in existing systems. In a multi-stage pipeline, an early layout mistake can affect later recognition. End-to-end vision-language systems, meanwhile, may generate redundant or hallucinated content and struggle with structural reasoning at high resolution. TeleOCR proposes combining geometric reasoning with recognition and explicitly separating structural learning from content learning for tables and formulas.
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Training and geometric representation
The project describes four training stages: document-parsing pretraining, deformation-aware training, separate learning of table and formula structure and content, and reinforcement learning using task-specific rewards. For distorted pages, its method represents layout regions with polygon outlines and page deformation with a grid of control points. The project also describes Curvature-Guided Douglas–Peucker Sampling for polygon vertex placement and Multi-node Consensus Voting to generate pseudo-labels from multiple parsers. These are descriptions of the proposed method; they do not establish that each component independently improves results in every deployment.
What the model can emit
Examples in the model card include ordinary text, tables in OTSL-style markup, LaTeX formulas, code blocks, page layout, polygon layout for distorted pages, and tables extracted from scientific charts. The NYU Shanghai RITS explainer describes a layout-first, recognition-second workflow that does not require a separate rectification model. It also describes the architecture as a Qwen2.5-VL vision encoder, a Qwen3-0.6B language model, and an MLP aligner trained from scratch; that is the explainer’s account of the design, not independent architectural validation. See the RITS explainer.
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What the reported benchmarks show
The following figures are reported by the TeleOCR team in its model card and discussed by the RITS explainer. They should be read as project-reported evaluations, not independent test results. Benchmark version, metric, evaluation setup, and comparison cohort affect what a score means.
| Benchmark or challenge | Project-reported result | How to interpret it |
|---|---|---|
| OmniDocBench v1.6 | 96.87 overall; the project table lists 0.027 text edit, 96.36 formula CDM, 97.05 table TEDS, 98.52 table TEDS-S, and 0.122 read-order edit. | TeleOCR model-card result, 2026. Metrics do not all run in the same direction: for edit measures, lower is better, while higher is generally better for the listed recognition and structure scores. The RITS account notes OvisOCR2 has a lower text-edit value and higher formula CDM. |
| Wild_OmniDocBench | 88.53 overall. | TeleOCR project result, 2026; comparisons depend on the cohort and evaluation setup in the project table. |
| PureDocBench | 78.41 overall. | TeleOCR project result, 2026. The RITS explainer describes this as an average across clean, digitally degraded, and real-degraded pages; it reports Gemini-3.1-Pro slightly higher on the real-degraded subset alone, 71.98 versus TeleOCR’s 70.85. |
| ICDAR 2026 Sci-ImageMiner Challenge | 41.81 weighted score; reported first place. | TeleOCR project result relayed by the RITS explainer. |
| EMNLP 2026 Dr.DocBench Challenge | 67.96. | The model card presents this as a self-run comparison using native weights. The RITS explainer cautions that it should not be described as a leaderboard placement. |
The OmniDocBench figures illustrate why an overall score alone is not enough: performance can differ by text, formulas, table structure, or reading order. A useful comparison should match benchmark version and evaluation setup, then examine task-level metrics, performance on photographed or degraded pages, model size, and deployment requirements.
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What the results do—and do not—establish
The RITS explainer says the release’s comparisons are the authors’ own and recommends checking competitor figures against benchmark repositories. It also flags apparently duplicated submetrics for HunyuanOCR-1.5 and PaddleOCR-VL-1.6 in the project table as a likely transcription error. Treat rankings as the TeleOCR team’s reported results rather than a blanket claim that it is best in class.
The paper’s abstract sets out the problem and proposed methods; it does not establish independent replication. The available project materials provide local inference examples, but do not establish a validated minimum GPU configuration, production throughput, or a full independent end-to-end reproduction. A benchmark score should therefore not be treated as a guarantee of speed, accuracy, or suitability for a particular document workflow.
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Access, naming, and license considerations
The model card’s release history says the weights and technical report appeared under the name NaviDC-OCR on 2026-08-17, followed by a rename to TeleOCR on 2026-09-10. It includes local inference examples and records a community GGUF conversion for llama.cpp. The model card also stated that the model was not deployed by an Inference Provider on that page when accessed. Repository ownership, version, provider availability, and license materials can change, so check the current project pages before adopting a particular release.
The RITS article dated 2026-09-29 reports that the release uses Apache 2.0. That is a project-reported license claim; check the current license files for the weights, code, and dependencies and confirm that they cover your intended use.
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When TeleOCR may be worth evaluating
- Consider a trial if your documents include photographed, curved, or otherwise distorted pages and you need structured outputs such as tables or formulas rather than plain OCR text.
- Compare task-level results if accuracy matters differently across text, formula recognition, table structure, and reading order. Use the same benchmark version and evaluation conditions when comparing alternatives.
- Validate with your own pages before relying on it in a production workflow. The reported tables do not establish results for every document type, hardware setup, or end-to-end deployment.
- Check the current release details before installation, including repository version, license files, inference options, and the project’s complete parsing instructions.
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