Datalab

State of the Art models for Document Intelligence

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Indra OCR

Indra OCR is a 1M-parameter OCR model that runs on CPU. It turns PDFs and images into markdown, text, or JSON with boxes, and it reads handwriting, 80+ languages, and pages at any angle.

• Accurate - 66.7 on OmniParseBench (71.4 with --math), ahead of PaddleOCR's full PP-StructureV3 pipeline (58.4). Its text score (46.7) is ahead of PaddleOCR's PP-OCR text models (35.8) and Tesseract (19.3).
• Fast - about 44 pages/s on 32 CPU cores, 15x Tesseract and 10x PaddleOCR's PP-OCR text models on the same machine type and pages, and about 210 ms per page on 4 cores (benchmarks)
• 80+ languages - Latin-script languages, Russian and the Cyrillic languages, Greek, Chinese, Japanese, Korean, Arabic, Persian, Urdu, Hebrew, Hindi and the Indic languages, Thai, Khmer and more (languages)
• Handwriting - handwritten pages and forms (25.2% on the OmniParseBench handwriting tests, see limitations)
• Real-world scans - sideways, upside down, tilted up to 45 degrees, light text on dark ground
• Layout - a box, a label and a reading order for every line and block (headings, lists, tables, captions, footnotes, page headers)
• Digital PDFs - reads the text layer in any script, and OCRs only what is missing
• Small - ONNX Runtime, no PyTorch, no GPU

Indra OCR is named for Indra.

Try Datalab's Managed Platform

Indra OCR is built for speed on CPUs. When you need the highest accuracy, on handwriting, math, complex tables and degraded scans, our managed platform runs Chandra, our state of the art OCR model, with zero data retention by default, SOC 2 Type 2, and custom BAAs.

If you have high volume workloads, we offer a batch processing service that can handle 1B+ pages a day.

Get started with $5 in free credits - sign up - or try Chandra in our public playground.

Quickstart

pip install indra-ocr

indra-ocr paper.pdf                    # markdown to stdout
indra-ocr *.pdf --out results/         # one .md per input, all cores
indra-ocr scan.png --format json       # lines, boxes, layout blocks, confidences

indra-ocr-viewer                       # local web viewer on http://localhost:8765

The first run downloads the models (about 76 MB) from Hugging Face.

Examples

Real Indra OCR outputs on OmniParseBench pages. Colored boxes are layout blocks with their label, faint blue boxes are lines, drawn from the JSON output with no edits.

See more examples, with the markdown output for each →

License

The code is Apache 2.0. The model weights use a modified OpenRAIL-M license (LICENSE).

Commercial usage

This code is Apache 2.0, and our model weights use a modified OpenRAIL-M license (free for research, personal use, and startups under $20M funding/revenue, cannot be used competitively with our API). To remove the OpenRAIL license requirements, or for broader commercial licensing, visit our pricing page here.

Installation

pip install indra-ocr

The default models are about 76 MB: 30 MB of networks and 46 MB of language models for unsure lines. The main recognizer, rec.onnx (Latin script), has about 1.25M parameters, and all default models together have 7.3M (the optional math models are extra).

For the latest code from GitHub instead: pip install git+https://github.com/datalab-to/indra.git. To let the viewer compare with Tesseract, install pip install "indra-ocr[tesseract]" and a Tesseract binary.

Python 3.10 or newer, tested on Linux (x86_64 and aarch64, Python 3.10 to 3.13) and macOS (Apple silicon). It installs ONNX Runtime, OpenCV and pypdfium2, and no PyTorch. The models download from Hugging Face on the first run and are cached. To use a local copy, set INDRA_OCR_MODEL_DIR or pass --model-dir.

Usage

CLI

indra-ocr paper.pdf                       # markdown to stdout
indra-ocr *.pdf --out results/            # one .md per input
indra-ocr scan.png --format json          # lines, boxes, layout blocks, confidences, timings
indra-ocr paper.pdf --format text         # plain text, one line per detected line
indra-ocr paper.pdf --mode ocr --timings  # ignore the text layer, OCR every page; timings on stderr
indra-ocr paper.pdf --keep-furniture      # keep page headers and footers in the markdown
indra-ocr *.pdf --out results/ --cores 8  # limit the cores (default: all usable cores)
indra-ocr paper.pdf --math                # write math as LaTeX (off by default; slower on math pages)

CLI Options:

• --out DIR: write one file per input (.md, .txt or .jsonl)
• --format [md|text|json]: output format (default: md)
• --mode [auto|ocr|text]: auto reads the text layer where it passes the checks and OCRs elsewhere (default). ocr never reads the text layer. text uses the text layer even when the checks fail
• --no-image-ocr: on text-layer pages, do not OCR text inside images
• --cores N: cores to use, one worker process each (default: all usable cores)
• --dpi: render resolution for PDFs (default: 200)
• --max-pages N: read at most N pages per file
• --password: password for encrypted PDFs
• --keep-furniture: keep page headers and footers in the markdown
• --math: write math as LaTeX ($...$); off by default, see Math
• --timings: print per-page source and time to stderr
• --model-dir: folder with the models (default: download)

The CLI continues past files it cannot read, and exits with 1 if any failed.

From python

import indra_ocr

conv = indra_ocr.load(threads=4)          # mode="auto" | "ocr" | "text"
for i, page in conv.convert("paper.pdf"):
    print(page.source, page.timings["total_ms"])
    print(page.markdown)                  # headings, lists, tables as HTML
    page.blocks                           # [{"label", "bbox", "text"}] in reading order
    page.lines                            # [Line(box, text, conf)] in reading order

An image file works the same way as a PDF (PNG, JPEG, TIFF, BMP, WebP; one page per TIFF frame). For an image already in memory, pass a grayscale uint8 numpy array to conv.ocr:

import cv2
import indra_ocr

conv = indra_ocr.load(threads=4)
for i, page in conv.convert("scan.png"):  # an image file
    print(page.text)

gray = cv2.cvtColor(cv2.imread("photo.jpg"), cv2.COLOR_BGR2GRAY)
page = conv.ocr(gray)                     # an image in memory
print(page.markdown)

Runnable examples: examples/ocr_image.py (one image to markdown, text or JSON with boxes) and examples/quickstart.ipynb.

For many pages or files, convert_many runs one single-threaded worker process per core, one page per task, and returns results in input order:

import indra_ocr

if __name__ == "__main__":                # worker processes re-import this file
    for path, result in indra_ocr.convert_many(["a.pdf", "b.pdf"], cores=None):
        if isinstance(result, indra_ocr.InputError):
            continue                      # unreadable or locked file
        for i, page in result:
            print(path, i, page.source)

Without the if __name__ == "__main__": guard (needed on macOS and Windows), convert_many warns and reads everything in one process.

indra_ocr.load takes path (model folder), threads, dpi, mode, image_ocr, furniture and math. conv.convert takes max_pages and password.

Viewer

indra-ocr-viewer --port 8765              # open http://localhost:8765

A local web viewer: drop in a PDF or an image, and each page shows its lines, layout blocks and reading order over the page, with the rendered markdown, the markdown source, the lines with their confidences, the JSON output and the time of each stage. The pipeline options (text layer or OCR, math, headers and footers, reading order, OCR of text inside images, dpi, page limit) are set in the page, and lines, blocks, block labels and reading order can each be shown or hidden. Files stay on your machine. --tesseract lets the page compare with Tesseract (needs pytesseract and a Tesseract install). Over SSH, forward the port: ssh -N -L 8765:localhost:8765 host.

Output

Every page is a Page with:

• markdown: headings (##), paragraphs, lists, and tables as HTML. Page headers and footers are left out unless furniture=True / --keep-furniture
• text: one line per detected line, in reading order
• lines: Line(box, text, conf), box is x0, y0, x1, y1 in image pixels
• blocks: layout blocks in reading order, {"label", "bbox", "text"}
• source: text (PDF text layer), ocr, hybrid (text layer + OCR of text inside images) or error
• rotation, skew: the quarter or half turn applied (degrees), and the small tilt straightened
• vertical: the page is set in vertical columns (Chinese, Japanese): it was read turned a quarter turn, and its boxes are on the page as it is
• timings: per-step milliseconds, with total_ms
• note: why the page took its path

Block labels: Text, Section-Header, Page-Header, Page-Footer, List-Group, Table, Caption, Footnote, Table-Of-Contents, Form. --format json writes one JSON object per page with all of these fields.

Languages

OCR (scans and images): we measured 95 languages on held-out printed pages: 83 read at character F1 0.90 or higher, and 67 at 0.95 or higher. Four recognizers, and a line classifier that sends each line to one of them:

• rec.onnx: Latin (about 50 languages, with the letters of Hausa, Fula, Akan, Azerbaijani and Uzbek and the tone marks of Vietnamese and Yoruba), Cyrillic, Greek, and handwriting in Latin script.
• rec_cjk.onnx: Chinese (simplified and traditional), Japanese (kana and kanji), Korean.
• rec_rtl.onnx: Arabic, Persian, Urdu, Pashto, Kurdish, Hebrew. Lines come out in logical order, and a right-to-left page reads its right column first.
• rec_world.onnx: Devanagari (Hindi, Marathi, Nepali), Bengali, Tamil, Telugu, Kannada, Malayalam, Gujarati, Gurmukhi, Odia, Thai, Lao, Khmer, Myanmar, Sinhala, Georgian, Armenian, Ethiopic.

When the classifier is not sure of a line, the main recognizer reads it too, and the more confident reading wins.

OmniParseBench, per script (tests in parentheses):

Most scripts have few tests, so one test moves a score by 2 to 8 points. On OmniDocBench v1.5 (OCR only, 1,651 pages, our run without the formula metric), the text edit distance is 0.114 on English pages and 0.237 on simplified Chinese pages (lower is better), and table TEDS is 0.47.

Vertical text. Chinese and Japanese pages set in vertical columns are read turned a quarter turn, so each column is a line for the CJK recognizer. Scans are found by the orientation model, digital PDFs by their text layer. page.vertical is set, and boxes are on the page as it is.

Text layer (digital PDFs): any script. We checked text mode on 1,125 pages from 75 language families. On pages whose layer passes the checks, Indra OCR keeps at least 98% of the layer's characters in every family, including Arabic, Hebrew, Persian, Urdu, Thai, Khmer, Lao, Myanmar, Georgian, Armenian, Ethiopic, the Indic scripts, Chinese, Japanese and Korean. Right-to-left lines keep their right-to-left order.

Limitations

Indra OCR is built for speed on clean print. It is not the right tool for:

• Complex tables. Tables with merged or spanning cells, cell text that wraps over several lines, nested headers, or no ruling lines can come out with cells in the wrong rows or columns. Simple grid tables read well.
• Handwriting. Handwritten pages read, but not well: 25.2% on the OmniParseBench handwriting tests, and about a third of the words on held-out handwritten pages. Cursive and faint pencil read worst.
• Scripts outside the OCR list. Syriac, Tibetan, Mongolian, Santali, Meitei and other scripts not listed under Languages work only through a digital PDF's text layer. Khmer and Malayalam OCR are fair (65 to 71 on OmniParseBench). Written Cantonese characters (嘅 咗 冇) are not in the Chinese recognizer.
• Latin languages with many diacritics. Some of them (Kurmanji, Czech, Lithuanian, Icelandic, Turkish, Slovak) read less well: the recognizer sometimes writes a close diacritic instead of the right one (ñ for ň, ú for ů, é for ė). English, French, German and Spanish are not affected.
• Degraded scans. Old, faded or low-resolution scans read poorly (29.5% on the OmniParseBench degraded-scan tests).

Also:

• Math comes out as plain text unless --math is on (see Math).
• Vertical pages read their columns right to left and their tiers top to bottom; a newspaper whose articles wind across tiers can come out in the wrong article order. Vertical scans read less well than horizontal ones.
• Very wide, banner-shaped pages read poorly.
• Labels inside engineering drawings often come out as small tables.
• Pages above 100 megapixels at 200 dpi render at a lower dpi, so small text on them can be lost.

For these documents, Chandra on Datalab is the accurate option.

Math

Math is off by default. With --math (or indra_ocr.load(math=True)), a small classifier finds the lines that hold math, and a second recognizer reads them as text with inline LaTeX (we have $x _ { i } \leq 1$).

On OmniParseBench (all 2,937 pages, default settings otherwise):

• With --math, Indra OCR is ahead of PaddleOCR's PP-StructureV3 on the headline, text, tables and layout. Without it, PP-StructureV3 leads on text.
• Tables lose 1.5 points: numbers in some table cells are read as math.
• --math is slower. On x86, one page at a time on 4 vCPUs in auto mode, the median page takes 357 ms with --math and 212 ms without. With one single-threaded worker per core, throughput is about 30% lower on 4 cores and 38% lower on 32 (measured in one container on 300 OmniParseBench pages).

The math models download only when --math is on.

Benchmarks

OmniParseBench

OmniParseBench has 16,288 pass/fail tests on 2,937 pages. The headline is the mean of the text, tables and layout families. All runs: x86, 4 vCPUs per engine, one page at a time, every engine on the same pages with the same scorer and its default settings.

• Indra OCR's accuracy comes from the benchmark's own Indra OCR adapter and scorer (omni-parse-bench 1.0.0rc1, dataset revision 961c0f89), as on the OmniParseBench leaderboard. ‡The OCR-only row and the other engines come from our runs of the same pages and scorer. The median ms column comes from the x86 run below.
• The PP-OCR text models and Tesseract output lines, not tables or layout blocks, so their table scores are 0 and their layout family is not scored.
• PP-StructureV3 is PaddleOCR's full document pipeline (layout, tables, formulas). Without --math, it reads text better than Indra OCR (55.6 vs 46.7). With --math, Indra OCR is ahead on text too (62.7). It takes about 190 times as long per page as Indra OCR's default mode. *One of its 150 shards stalled for 6 hours on its first page, so its row leaves out 19 pages.
• Faster settings exist, but we did not score their accuracy. Tesseract with the eng model and 1 thread takes 3.4 s a page (6.1 s as run). PP-StructureV3 without formula, seal and chart recognition, unwarping and orientation, and with its mobile OCR models, takes 10.5 s a page. Indra OCR is still 16-50x faster per page.
• The OmniParseBench leaderboard's own Tesseract run scores 17.6 on text.
• Median latency varies by about 15% between runs of the same code, so compare engines within one run. The Indra OCR rows come from one run. The other engines ran earlier, on the same container type and pages.

Chinese (136 OmniParseBench Chinese tests): Indra OCR 64.0, PaddleOCR PP-OCRv5 mobile Chinese text models 65.4, PP-StructureV3 68.4.

olmOCR-bench

Born-digital splits and old scans, our harness, 200 dpi:

Throughput

The CLI and convert_many run one single-threaded worker process per usable core, one page per task, so a single long PDF also uses every core. --cores limits it.

Pages per second on x86 (AMD EPYC 9004 Genoa, 34 cores, no GPU), on the same 300 OmniParseBench pages, with one single-threaded worker per core, OCR mode and default settings. The other engines ran once on the same machine type and pages.

At 32 cores, Indra OCR is 10x PaddleOCR's PP-OCR text models and 15x Tesseract. *PP-StructureV3 ran a time-capped subset of other OmniParseBench pages in an earlier run, so its numbers are upper bounds.

Throughput depends on the CPU: on other x86 container types we measured 25 to 51 pages per second at 32 cores.

Model loading and one warm-up page per worker are not timed.

Reproducing

git clone https://github.com/datalab-to/indra.git && cd indra
pip install indra-ocr 'omni-parse-bench[benchmark]'
python bench/opb_indra.py --out runs/opb_auto --mode auto --procs 8

Each page runs in a single-threaded worker process, page headers and footers kept. --mode ocr gives the OCR-only row, --math the --math row, --suites scores only the listed suites, --limit N scores the first N pages, and --models points at a local model folder. The run writes summary.json (the benchmark's own summary, plus median and p90 ms per page) and pages.jsonl (per page: time, source, tests passed). The tables above come from our x86 harness, which gives each page 4 threads. This script gives each page one thread, so its times differ, and its scores can differ by a few tenths of a point.

Credits

Thank you to the following open source projects:

• ONNX Runtime
• pypdfium2
• OpenCV
• Hugging Face Hub