enclave-scribe-devanagari-iter4

EnclaveScribe — Devanagari OCR (iter-4)

A LoRA adapter for allenai/olmOCR-2-7B-1025 that adds page-level Devanagari OCR capability. Iter-4 is fine-tuned on top of iter-3 with real Indic document page images plus word-level replay, and fixes iter-3's known dead-loop failure mode on long dense Hindi/Marathi pages.

Built by Enclave Labs. MIT-licensed. Part of the EnclaveScribe project — a self-hostable, Indic-first document OCR system.

What it does

Given an image containing Devanagari text (Hindi, Marathi, Sanskrit, Nepali, Pali), returns the Unicode transcription. Unlike iter-3 which was trained on word crops only, iter-4 also handles full document pages without falling into generation loops.

For full-page PDFs, use it via the EnclaveScribe agent pipeline which handles page rasterization and generation-config.

Results — page-level ship-gate

Tested against a 6-page Gazette of India Extraordinary notification (dense Hindi + English) with the runtime bad_words_ids workaround disabled:

Iter-3 pages 4 and 5 exhausted the full generation budget emitting <tool_call> blocks and produced empty output after the post-hoc regex strip. Iter-4 completes every page inside the budget with real document text.

Iter-4 is also more faithful to source script conventions — for example, it preserves Arabic numerals inside English sections (where iter-3 hallucinates Devanagari digits like २११३ instead of 2113).

See the full head-to-head in reports/iter4/GAZETTE_TEST.md.

How to use

import torch
from transformers import AutoProcessor, AutoModelForImageTextToText
from peft import PeftModel
from PIL import Image

BASE = "allenai/olmOCR-2-7B-1025"
ADAPTER = "enclavelabs/enclave-scribe-devanagari-iter4"

processor = AutoProcessor.from_pretrained(BASE)
model = AutoModelForImageTextToText.from_pretrained(
    BASE, dtype=torch.bfloat16, device_map="auto"
)
model = PeftModel.from_pretrained(model, ADAPTER)
model.eval()

image = Image.open("hindi_page.png").convert("RGB")
messages = [{
    "role": "user",
    "content": [
        {"type": "image", "image": image},
        {"type": "text",  "text": "Extract this document page as clean markdown. "
                                   "Preserve the original script exactly."},
    ],
}]
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=[text], images=[image], return_tensors="pt").to(model.device)

with torch.no_grad():
    out = model.generate(
        **inputs,
        max_new_tokens=4096,
        do_sample=False,
        repetition_penalty=1.1,   # belt-and-suspenders; iter-4 no longer strictly needs it
    )

print(processor.batch_decode(
    out[:, inputs.input_ids.shape[1]:], skip_special_tokens=True
)[0].strip())

Note: iter-4 no longer dead-loops on the pages that broke iter-3, but we recommend keeping repetition_penalty=1.1 in production. It costs nothing and stays as insurance.

Training details

• Base model: allenai/olmOCR-2-7B-1025
• Starting point: iter-3 LoRA adapter (enclavelabs/enclave-scribe-devanagari), loaded via resume_adapter so training continues rather than starting fresh
• Method: LoRA (r=32, α=64, inherited from iter-3)
• Trainable parameters: ~95M of 8.4B (~1.1%)
• Training data:
  • Pages — 793 pseudo-labeled Devanagari page images from ai4bharat/indicdlp (Hindi + Marathi), labeled by iter-3 through the EnclaveScribe agent pipeline; quality-filtered from 2,000 attempts (41.75% pass rate)
  • Word replay — 500 word-level samples from himalaya-ai/devanagari_ocr_dataset mixed in at a 5:1 pages:words ratio to prevent catastrophic forgetting on iter-3's word-level competency
  • Total: 1,254 training samples + 42 held-out val
• Hardware: 1× NVIDIA A10G 24GB (AWS g5.xlarge on-demand)
• Runtime: 1h37m, 2 epochs, 82 steps
• Precision: bf16 + Liger kernel + gradient checkpointing
• Effective batch: 32 (per-device 1 × grad accum 32)
• Optimizer: AdamW, cosine LR schedule, LR 5.0e-5, 10 warmup steps
• max_length: 8192 (up from iter-3's 4096; pages need ~1500–2500 text tokens + ~640 image tokens)
• Compute cost: ~$3 training only; ~$65 including the pseudo-labeling pass

Limitations

• Pseudo-label ceiling: training labels came from iter-3, which has 17.4% CER on word crops. Iter-4 inherited character-level errors from those labels. Real human labels would break this ceiling in a future iteration.
• Training loss stayed flat at ~4.2 across all 82 steps. Iter-4's improvements are class-of-failure fixes (no more dead-looping) rather than a broad character-level uplift. Iter-4 is "iter-3 that doesn't break on long pages", not "iter-3 but sharper".
• English regression: not measured against a held-out English benchmark. Qualitative testing on a bilingual Hindi/English gazette PDF suggests English is preserved and Arabic numerals are handled correctly, but this is not a formal claim.
• Not evaluated on: Tamil, Telugu, Kannada, Bengali, Gujarati, Punjabi, other Indic scripts. This adapter is Devanagari-family only.
• Ship-gate scope: verified on a 6-page bilingual gazette PDF. Longer documents, tables, and structure preservation not exhaustively benchmarked.

Iter-5 plans

• Real human-labeled pages (300–500 samples) to break the pseudo-label ceiling, OR
• Bigger base model (e.g. allenai/olmOCR-2-32B-1025) with the same corpus for more LoRA capacity
• English regression benchmark for both iter-3 and iter-4
• Regression gate: every future iteration runs against tests/fixtures/pdfs/gazette_moef_2024_06_07.pdf before shipping

Follow github.com/Enclave-Labs-Inc/enclave-scribe for updates.

Citation

@software{enclavescribe_devanagari_iter4_2026,
  title  = {EnclaveScribe: Self-hostable Indic OCR — Devanagari adapter (iter-4)},
  author = {Enclave Labs},
  year   = {2026},
  url    = {https://huggingface.co/enclavelabs/enclave-scribe-devanagari-iter4},
}

License

MIT — same as the base model and the EnclaveScribe repo.