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.
--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).Indra OCR is named for Indra.
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.
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.
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 →
The code is Apache 2.0. The model weights use a modified OpenRAIL-M license (LICENSE).
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.
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.
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.
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.
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.
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-furnituretext: one line per detected line, in reading orderlines: Line(box, text, conf), box is x0, y0, x1, y1 in image pixelsblocks: layout blocks in reading order, {"label", "bbox", "text"}source: text (PDF text layer), ocr, hybrid (text layer + OCR of text inside images) or errorrotation, skew: the quarter or half turn applied (degrees), and the small tilt straightenedvertical: 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 istimings: per-step milliseconds, with total_msnote: why the page took its pathBlock 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.
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):
| Script | Score | Script | Score |
|---|---|---|---|
| Latin (9,406) | 61.6 | Thai (39) | 87.2 |
| Chinese (136) | 64.0 | Lao (52) | 96.2 |
| Korean (24) | 79.2 | Khmer (34) | 70.6 |
| Japanese kana (19) | 57.9 | Tamil (25) | 84.0 |
| Devanagari (23) | 69.6 | Telugu (33) | 87.9 |
| Bengali (40) | 77.5 | Kannada (48) | 93.8 |
| Arabic (13) | 69.2 | Malayalam (40) | 65.0 |
| Hebrew (30) | 80.0 | Gujarati (15) | 80.0 |
| Georgian (14) | 100 | Gurmukhi (31) | 64.5 |
| Armenian (60) | 100 | Ethiopic (43) | 100 |
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.
Indra OCR is built for speed on clean print. It is not the right tool for:
Also:
--math is on (see Math).For these documents, Chandra on Datalab is the accurate option.
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):
| headline | text | tables | layout | math tests | |
|---|---|---|---|---|---|
| Indra OCR | 66.7 | 46.7 | 61.1 | 92.2 | 14.0 |
Indra OCR --math | 71.4 | 62.7 | 59.6 | 92.0 | 55.8 |
| PaddleOCR PP-StructureV3 | 58.4 | 55.6 | 47.8 | 71.7 | – |
--math, Indra OCR is ahead of PaddleOCR's PP-StructureV3 on the headline, text, tables and layout. Without it, PP-StructureV3 leads on text.--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.
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.
| engine | headline | text | tables | layout | median ms / page |
|---|---|---|---|---|---|
| Indra OCR (auto: text layer + OCR) | 66.7 | 46.7 | 61.1 | 92.2 | 212 |
| Indra OCR (OCR only)‡ | 61.2 | 40.7 | 52.7 | 90.3 | 344 |
Indra OCR --math (auto) | 71.4 | 62.7 | 59.6 | 92.0 | 357 |
| PaddleOCR PP-OCRv6 small, text models only (RapidOCR) | – | 35.8 | 0.0 | – | 2,685 |
| PaddleOCR PP-StructureV3 (2,918 pages*) | 58.4 | 55.6 | 47.8 | 71.7 | 40,958 |
| Tesseract 5 (our run, Latin model) | – | 19.3 | 0.0 | – | 5,496 |
--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.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.Chinese (136 OmniParseBench Chinese tests): Indra OCR 64.0, PaddleOCR PP-OCRv5 mobile Chinese text models 65.4, PP-StructureV3 68.4.
Born-digital splits and old scans, our harness, 200 dpi:
| split | Indra OCR | Tesseract 4 |
|---|---|---|
| headers_footers | 91.2 | 35.5 |
| multi_column | 70.0 | 70.4 |
| long_tiny_text | 67.9 | 64.0 |
| table_tests | 60.5 | 1.0 |
| old_scans | 20.0 | 18.4 |
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.
| engine | 4 cores | 8 cores | 16 cores | 32 cores |
|---|---|---|---|---|
| Indra OCR | 6.2 | 11.9 | 23.7 | 43.9 |
| PaddleOCR PP-OCRv6 small, text models only (RapidOCR) | 0.65 | 1.24 | 2.42 | 4.28 |
| Tesseract 5 | 0.78 | 1.37 | 2.27 | 2.92 |
| PaddleOCR PP-StructureV3* | ≤ 0.022 | ≤ 0.044 | ≤ 0.089 | ≤ 0.18 |
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.
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.
Thank you to the following open source projects: