Architecture

Two models with a JSON file between them.

page image ──> IndicDocLayout ──> layout JSON ──> IndicBlockOCR ──> page JSON + markdown

IndicDocLayout finds the blocks and orders them. IndicBlockOCR transcribes one block crop per request. Everything between the two models (cropping, prompt selection, reassembly) is this package.

The call path

One page, top to bottom, with the file that owns each step.

IndicDocParser.parse(image)                              idp_offline.py

  IndicDocLayout.detect(image)                           idp_offline.py
    _open                     image -> PIL RGB           idp_offline.py
    IndicDocLayoutBackend.detect                         idp_layout.py
      infer                   -> [y0,x0,y1,x1] at 0-1000 idp_model_infer.py
      convert + clamp_to_page -> pixel [x0,y0,x1,y1]     idp_layout.py, blocks.py
      clean_layout            drop duplicate boxes       idp_blocks.py
      _densify                order -> gap-free 0..n-1   idp_layout.py
    = PageResult, every block has text None              idp_types.py

  IndicBlockOCR.run(image, layout)                       idp_offline.py
    _as_page                  dict -> PageResult, validated
    is_transcribed            drop OCR_SKIP_LABELS       idp_contract.py
    resolve_nested_equations  drop nested equations      idp_blocks.py
    build_requests            -> [CropRequest]           idp_recognizer.py
      crop_for + area_clamp   block -> image             idp_crops.py
      prompt_for(block.type)  type  -> prompt            idp_contract.py
    backend.transcribe        [CropRequest] -> [str]     a recognizer backend
    match by order            texts -> blocks            idp_offline.py
    reconstruct               blocks -> markdown         idp_reconstruct.py
  = PageResult, every block has text

Blocks that were skipped are not deleted. They come back with text: "".

Files by role

Contract and data. The vocabulary everything else shares.

file	holds
`idp_contract.py`	prompts, the label to type map, which labels are skipped
`idp_types.py`	`Block`, `PageResult`, and every tunable config

The layout model. Torch lives here and nowhere else.

file	holds
`idp_model_infer.py`	preprocessing and decode for one page
`idp_model_labels.py`	the 37 classes, pure Python
`idp_model_ppdoc.py`	the PP-DocLayoutV3 subclass
`idp_model_order_loss.py`	reading-order decode, training loss

Geometry and crops. No model, no network.

file	holds
`idp_blocks.py`	box math, `clean_layout`, nested-equation dedup
`idp_crops.py`	`crop_for` and `area_clamp`

Backends. Two Protocols, five implementations, one per deployment.

class	file	used by
`IndicDocLayoutBackend`	`idp_layout.py`	the real detector
`JsonLayoutBackend`	`idp_layout.py`	replaying a layout file, no torch
`HfRecognizer`	`idp_recognizer.py`	plain transformers, the Hub package

Orchestration.

file	holds
`idp_offline.py`	`IndicDocLayout`, `IndicBlockOCR`, `IndicDocParser`
`idp_recognizer.py`	`CropRequest`, `build_requests`, the recognizer Protocol
`idp_reconstruct.py`	blocks to markdown, math and hyphen repair

Invariants

Two box conventions. The model emits [y0, x0, y1, x1] normalised to 0-1000. The pipeline uses pixel [x0, y0, x1, y1].
order must be gap-free and 0-based. Transcriptions are matched back to blocks by position, so a gap or a duplicate moves text onto the wrong block.
The crop clamp is on area, not on a side. Pinning a side explodes elongated crops. See CropConfig in idp_types.py.
type selects the prompt. A wrong type changes what the model was asked to do, not just how a block is labelled. Unrecognised labels are rejected for this reason.