Indra OCR 0.3.1

Install: pip install indra-ocr. Models: Hugging Face datalab-to/indra-ocr, tag v3.1.

0.3.1

• A model download that fails (no network, no access) or a model folder with
  missing files raises indra_ocr.ModelError with one clear message; the CLI
  and the viewer print it as one line and exit with 1.
• --version for indra-ocr and indra-ocr-viewer.
• page.skew is rounded to 0.01 degree.
• Project links on PyPI, and absolute image URLs in the PyPI description, so
  its images show there.
• README: install from PyPI (pip install indra-ocr); a Limitations section
  that leads with complex tables, handwriting and scripts outside the OCR list.

0.3.0 (first release)

Models v3 (Hugging Face datalab-to/indra-ocr, tag v3; v3.1 adds the math models).

• Viewer. A Datalab-styled page with the pipeline options in the page
  (source, math, headers and footers, reading order, OCR inside images, dpi,
  page limit), show/hide for lines, blocks, block labels and reading order, and
  tabs for the rendered markdown, its source, the lines and the JSON.

• convert_many no longer hangs when its worker processes cannot start (with
  the spawn start method, code not under if __name__ == "__main__":, or code
  from stdin): it warns and reads the remaining inputs in the calling process.

• Math (opt-in). --math / load(math=True): a line classifier
  (mathcls.onnx) and a math recognizer (math_enc / math_dec_init /
  math_dec_step.onnx, attention decoder with cached keys and values) write
  lines with math as text with inline LaTeX. A reading replaces a line only
  with math in it and a mean token probability of 0.9 or more. Omni Parse
  Bench: headline 62.1 -> 66.8, text 41.4 -> 57.5, math tests 14.3 -> 56.5,
  tables 56.4 -> 54.8; median page ~210 -> ~360 ms (x86, 4 cores, auto mode).
  Off by default; the math models download only when it is on.

• Orientation. orient.onnx turns a page that is turned by a quarter or
  half turn upright before detection (page.rotation). A turn needs at least
  95% from the model. Omni Parse Bench: rotated content 3.5% -> 24.3%.

• All cores. The CLI and the new indra_ocr.convert_many run one
  single-threaded worker process per usable core, one page per task (a single
  PDF too); --cores limits them. 40 documents: 30.0 s on 1 core, 8.7 s on 4,
  2.8 s on 16 (one process with ORT threads gained at most 1.2x beyond 4
  cores). The CLI's --threads is now an alias of --cores.

• Deskew. A page tilted by up to 5 degrees is straightened before
  detection (page.skew, about 8 ms a page); boxes are mapped back to the
  page as turned. Omni Parse Bench rotation_skew suite: 60.0 -> 76.4.

• Low-confidence lines get no text. A recognized line under 0.5 mean
  confidence (empty form fields, checkboxes, rules read as "-", "I", "1")
  keeps its box but no text.

• Orientation from a broken text layer. A PDF page OCR'd for a broken font
  map is not turned when its glyphs run upright (orient.onnx turns about half
  of upright Arabic and Hebrew pages).

• CJK markdown. Lines of Chinese or Japanese join without a space; a
  bullet detected as its own line starts a list item.

• Table cells escape only &, < and > (no ').

• Text inside diagrams and charts. The detector finds 51% of the text
  inside pictures (12.5% before).

• Right-to-left text layers. Runs in a right-to-left line are ordered right
  to left.

• OCR only where it can read. A text layer in a script the recognizer
  cannot read is kept even when a trust check fails (not for broken font
  maps), and empty lines on such pages are not OCR'd into junk.

• Layout blocks. page.blocks lists the layout blocks (label, box, text)
  in reading order; the CLI's JSON output includes them.

• --keep-furniture keeps page headers and footers in the markdown.

• Optional models: script.onnx + rec_cjk.onnx route lines with Chinese
  characters to a Chinese recognizer; mask.onnx (CenterMask) masks
  neighbouring lines before recognition. Both run only when present.

• bench/opb_indra.py: Omni Parse Bench runner.

• Robust inputs (release test: 4,340 benchmark pages, 1,177 documents with
  16,017 pages, 37 edge cases): the file type comes from the file's bytes;
  multi-page TIFFs give every page; pages above 100 MP render at a lower dpi;
  unreadable, corrupt or password-protected files raise InputError
  (password= / --password); a page that fails gives an empty page with
  source == "error" instead of ending the document; the CLI continues past
  failed files and exits 1. Text outside a PDF's crop box is not read.

• Faster on dense pages: reading-order clustering is O(n^2) instead of
  O(n^3) (a 1,984-line page: 53 s -> 0.45 s).

• Bounded memory: ONNX Runtime's CPU arena is off (it grew without bound
  on changing batch shapes; a 40-page large scan: 4.9 GB -> 1.4 GB).