| --- |
| license: other |
| license_name: surya-ocr-2-license |
| license_link: https://huggingface.co/datalab-to/surya-ocr-2 |
| base_model: datalab-to/surya-ocr-2 |
| tags: |
| - ocr |
| - electoral-roll |
| - india |
| - surya |
| - mlx |
| - vlm |
| pipeline_tag: image-to-text |
| library_name: mlx-vlm |
| --- |
| |
| # savitr — terse electoral-roll OCR (distilled Surya) |
|
|
| `gojiberries/savitr` is [`datalab-to/surya-ocr-2`](https://huggingface.co/datalab-to/surya-ocr-2) |
| (650M Qwen3.5-VL-style OCR) **self-distilled** to read Indian electoral-roll pages and emit one |
| compact, pipe-delimited line per voter instead of verbose HTML: |
|
|
| ``` |
| epic|name|relation(F/H/M)|relative_name|house|age|sex |
| ``` |
|
|
| That is ~5× fewer decode tokens than the HTML output, so it runs ~2.7× faster end-to-end at the |
| teacher's accuracy. Converted to **MLX** 8-bit for Apple Silicon. |
|
|
| ## Usage |
|
|
| ```bash |
| pip install savitr |
| savitr ocr roll.pdf --terse # auto-downloads this model |
| ``` |
|
|
| ```python |
| from huggingface_hub import snapshot_download |
| from savitr import MLXSuryaOCR, parse_terse |
| from savitr.rolls.parse import TERSE_PROMPT |
| path = snapshot_download("gojiberries/savitr") |
| eng = MLXSuryaOCR(path, prompt=TERSE_PROMPT) |
| voters = parse_terse(eng.ocr_image("page.png")[0]) |
| ``` |
|
|
| ## How it was trained |
|
|
| Teacher = full Surya (`surya-ocr-2`) OCRs roll pages to HTML; a parser cleans them into terse |
| targets; the model is **LoRA-fine-tuned** on (page image → terse rows) — 450 pages drawn from |
| constituencies held out of the eval, 1 epoch, for $0 on a free Kaggle T4. The terse format is the only |
| behavioral change — reading ability is inherited from Surya. |
|
|
| ## Evaluation (out-of-sample, vs the Surya teacher) |
|
|
| Held-out constituencies never seen in training (37 pages, 1,076 teacher voters): |
|
|
| | Field | Fidelity | | Field | Fidelity | |
| |---|---|---|---|---| |
| | voter recall | 99.3% | | relative name | 96.2% | |
| | EPIC | 97.2% | | relation code (F/H/M) | 97.9% | |
| | name | 96.2% | | house | 98.8% | |
| | age | 97.5% | | sex | 98.2% | |
|
|
| Per-voter record similarity **98.7%**, whole-page similarity **92.9%** (1 − normalized edit distance). |
| Fidelity = agreement with the teacher's output; absolute accuracy ≈ these × Surya's own ~93–95%. |
|
|
| ## Limitations |
|
|
| **v0.2 (450 training pages, AC-holdout).** All fields are teacher-grade out-of-sample — EPIC, house, |
| age, sex, the relation code, names, and recall all 96–99% (the relation code, the weak spot of the |
| earlier 77-page model, is now 98%). Trained on **Manipur 2025 English** rolls; other states/scripts are |
| out of distribution. Pair with savitr's value-anchored `parse_terse`, which stays column-aligned even |
| when the model drops a field. |
|
|
| ## License & attribution |
|
|
| Derived from `datalab-to/surya-ocr-2`; its license governs use of these weights. savitr's *code* |
| is MIT. Electoral rolls are public records published by the Election Commission of India. |
|
|
