---
license: apache-2.0
tags:
  - object-detection
  - document-layout-analysis
  - indic
  - yolo
  - doclayout-yolo
  - document-ai
library_name: doclayout-yolo
pipeline_tag: object-detection
---

# DocLayout-YOLO-Indic

**Real-time document layout detection for 12 Indic scripts**, built on DocLayout-YOLO (YOLOv10-m + GL-CRM).

This repository accompanies an M.Tech dissertation (Vignesh P, BITS Pilani WILP). It contains four model
checkpoints that together form a **controlled ablation study** — and its central result is an honest
**negative finding**: the two proposed adaptation techniques did *not* improve over a simple baseline.
That finding, proven and explained, is the main scientific contribution.

---

## 1. What problem does this solve?

Document Layout Analysis (DLA) is the first step in digitising any document: before OCR can read the text,
a model must find the *structure* — which regions are paragraphs, headings, tables, figures, lists, and so on.
Every downstream stage inherits the errors of this step.

Fast, modern DLA detectors are trained almost entirely on **English and Chinese** documents and generalise
poorly to **Indic scripts**, because those scripts are *visually* different:

- **Shirorekha** — the continuous horizontal headstroke of Devanagari, Bengali and Gurmukhi — looks like a table border to a Latin-trained model.
- **Conjunct consonants and stacked vowel marks (matras)** break line-height assumptions.
- **Urdu** is written right-to-left.
- Real pages **mix scripts** and contain **rare region types** (sidebars, pull-quotes).

Over 1.4 billion people use Indic languages, yet no public real-time detector had been demonstrably adapted
*and* evaluated across the major scripts. This project builds one — and rigorously tests whether its own
adaptation ideas actually help.

---

## 2. The model

- **Architecture:** DocLayout-YOLO = YOLOv10-m backbone + **GL-CRM** (Global-to-Local Controllable Receptive Module) + PAN-FPN neck + dual (one-to-many / one-to-one) detection heads.
- **Parameters:** ~19.97 M (20.01 M at 42 classes).
- **Input:** 1024×1024 (also evaluated at 640 and 1280).
- **Vision-only:** it does not read text or depend on OCR — it responds purely to visual structure.
- **Speed:** >100 FPS on an NVIDIA A100 — real-time.

---

## 3. The checkpoints (a controlled ablation)

The four files differ **only** in what training happened *before* the final fine-tuning on the IndicDLP
benchmark. Because everything else is held identical, comparing them isolates the effect of each
proposed contribution — this is what licenses causal, not correlational, conclusions.

| File | Training pipeline | Classes | Val mAP@[.5:.95] | **Test mAP@[.5:.95]** |
|------|-------------------|---------|------------------|------------------------|
| **`config_A_reported_42cls.pt`** | Public checkpoint → fine-tune. **No** synthetic pretraining, **no** self-training. **← the reported model** | 42 | 0.379 | **0.364** |
| `config_B_synthetic_42cls.pt` | Synthetic pretrain → fine-tune. *Isolates synthetic pretraining.* | 42 | 0.353 | 0.252 |
| `config_C_fullpipeline_42cls.pt` | Synthetic → self-train → fine-tune (the full proposed pipeline). | 42 | 0.328 | — |
| `selftrained_intermediate_9cls.pt` | Intermediate self-trained checkpoint, *before* fine-tuning. | 9 | — | 0.577 (in-domain, Bengali) |

**Which to use:** for inference, use **`config_A_reported_42cls.pt`** — it is the best model and the one the
dissertation reports. (It is a stripped, deployment-ready checkpoint, ~41 MB; the others retain optimizer
state and are ~242 MB.)

### The two class ontologies
- **9 classes** (intermediate stages): `text_body, headline, table, figure, caption, advertisement, sidebar, pull-quote, decorative-frame` — a coarse, script-agnostic set used for synthetic pretraining and self-training.
- **42 classes** (final): the full IndicDLP ontology, grouped into seven families — body text, heading hierarchy, list hierarchy, figures/tables/formulae, Q&A structure, navigation/metadata, and publishing furniture.

The detection head is **re-shaped** between stages (27 → 9 → 42 classes); the backbone and neck weights
transfer across all stages, and only the final classifier changes width.

---

## 4. Headline results (reported model, `config_A`)

- **0.525 mAP@0.5** and **0.364 mAP@[0.5:0.95]** on the **11,633-image** IndicDLP test set (180,039 instances).
- **Consistent across all 12 scripts** — per-script mAP@[.5:.95] spans only **0.304–0.426**, with no script failing catastrophically. (Best: Assamese 0.426; lowest: English 0.299.)
- **>100 FPS** on A100 — real-time preserved.
- **Precision 0.590 / Recall 0.506** overall.

### Scalability (accuracy vs. speed)
| Input size | Test mAP@[.5:.95] | Note |
|------------|-------------------|------|
| 640 px  | 0.329 | fastest; ~90% of peak accuracy at a fraction of the latency |
| 1024 px | **0.364** | peak accuracy (training resolution) |
| 1280 px | 0.353 | strictly dominated — slower *and* less accurate (train-test resolution mismatch) |

---

## 5. The key finding (a rigorous negative result)

**Neither proposed contribution improved over plainly fine-tuning the public checkpoint.**
Config A (no synthetic, no self-training) is the best model — by **+11.2 mAP** on the test set over Config B.

**This is statistically significant across all 12 scripts:**
- Wilcoxon signed-rank: **p < 0.001** (p = 0.00049)
- Paired t-test: **p < 10⁻¹⁰**
- Cohen's d = **7.37** (a very large effect)
- All 12/12 scripts improved under Config A; 95% CI on the gain [0.096, 0.114].

**Mechanism — catastrophic forgetting.** The validation→test gap *widens* from 2.6 to 11.2 mAP under
synthetic pretraining — the signature of damaged generalisation, not mere no-gain. The synthetic corpus
(23 templates, 9 classes) is narrower than the base model's original DocSynth-300K pretraining; 15 epochs to
0.985 synthetic-validation over-specialised the backbone, and the coarse 9-class intermediate ontology
erased fine distinctions the 42-class task then had to relearn from only 12,082 fine-labelled images.

> **In short:** the techniques failed, but the science succeeded — a falsified hypothesis, proven under
> controlled conditions and mechanistically explained, that saves others from the same mistake.

### Note on `config_B` and `config_C`
These are provided **for transparency and reproducibility of the ablation** — they are *not* recommended for
deployment, as they under-perform Config A. They let others verify the negative result independently.

---

## 6. Known limitations

- **Hierarchical-depth confusion is the dominant error.** The model localises headings/lists but cannot infer nesting *depth* — recall collapses with depth (section-title 0.66 → sub 0.23 → subsub 0.01). This is **architectural** (a single-shot detector classifies from local appearance; depth is relational) and **not Indic-specific** — it affects English identically.
- **Urdu is untested.** Urdu was synthesised and trained on, but the IndicDLP benchmark contains no Urdu, so right-to-left performance is unevaluated.
- **An auxiliary script-classification head** was designed but not implemented (future work).
- Results use a **12,082-image training subset** (compute-bounded); the reported mAP is therefore a lower bound, not a ceiling.

---

## 7. Usage

```python
from huggingface_hub import hf_hub_download
from doclayout_yolo import YOLOv10

# download the reported model from this repo
ckpt = hf_hub_download("VigneshPR/doclayout-yolo-indic", "config_A_reported_42cls.pt")
model = YOLOv10(ckpt)

# run on a document page
result = model.predict("page.jpg", imgsz=1024, conf=0.25)[0]
result.plot()                      # annotated image (numpy array, BGR)
print(len(result.boxes), "regions")
for c in result.boxes.cls.tolist():
    print(result.names[int(c)])
```

Install the runtime:
```bash
pip install git+https://github.com/opendatalab/DocLayout-YOLO.git
```

---

## 8. Training setup

- **Platform:** Google Colab Pro+ (no institutional cluster). Training on **A100 40 GB**; evaluation on **Tesla T4** to conserve compute.
- **Data engineering under a 15 GB Drive limit:** partial-tar extraction reduced the 42 GB IndicDLP download to ~5 GB; bulky image sets kept on ephemeral local disk, only labels/checkpoints persisted.
- **Fine-tuning:** ~30 epochs per config, batch 16 @ 1024, AMP, identical settings across all three ablation configs.
- **Self-training (CBST):** class-balanced pseudo-labelling on the unlabelled BaDLAD Bengali pool, with provably disjoint splits (leakage-audited: 20,365 − 10,000 = 10,365).

---

## 9. Related resources

- **Synthetic corpus:** [VigneshPR/indicsynth-150k](https://huggingface.co/datasets/VigneshPR/indicsynth-150k) — the 150,000-page SA-MCB dataset used for pretraining.
- **Benchmark (working mirror):** [VigneshPR/IndicDLP](https://huggingface.co/datasets/VigneshPR/IndicDLP) — mirror of the IndicDLP benchmark (original authors: IndicDLP, ICDAR 2025); credit and licence belong to them.

---

## 10. Citation

If you use these models or the finding, please cite the dissertation:

```bibtex
@mastersthesis{vignesh2026doclayoutindic,
  title  = {DocLayout-YOLO-Indic: Cross-Script Document Layout Analysis for Indic Scripts
            using Synthetic Pretraining, Self-Training and Controlled Ablation},
  author = {Vignesh P},
  school = {BITS Pilani (WILP)},
  year   = {2026}
}
```

*Built on [DocLayout-YOLO](https://github.com/opendatalab/DocLayout-YOLO) (YOLOv10-m + GL-CRM).*