OpenThai2.0-OCR-Text

Fast Thai + English printed-text OCR that runs on a CPU. It is the engine behind iApp Document OCR v3.0, released as open weights with its runtime.

• Detector: PP-OCRv6-tiny (1.8 MB).
• Recognizer: a Thai CTC line recognizer trained by iApp (2.4 MB).
• Runtime: ONNX models on OpenVINO (or ONNX Runtime), CPU only. About 0.5 CPU-seconds per dense A4 page, roughly 950 pages a minute on 8 Xeon cores.

It reads a page image and returns its text in reading order, with a box and a confidence for every line. It does not write tables or Markdown; for that, see OpenThai2.0-OCR-Markdown.

Step-by-step guide for Windows, Mac and Linux (installing Python, reading pictures and PDFs, a ready script): English · ภาษาไทย · 中文

Model at a glance

How it compares

Every engine read the same pages and was scored the same way. The score is character accuracy, 100 × (1 − CER), so higher is better, against checked gold, after markup is stripped from both sides; reading order counts. A page an engine fails on counts as empty.

The 12 printed sets: parliament budget documents and Royal Gazette pages (each as a hand-verified set and as a larger set), SEA-DocBench Thai, Wikisource old books, Administrative Court judgments (50 documents), ThaiOCRBench full pages, th-docs-tables text-only pages, and three OCR104 sets (document crops, Thai+English crops, document photos). The table shows six of them, with handwriting, which is not in the average.

Bold marks the best score in each column. The average is over the 12 printed sets listed above the table; handwriting is not in it.

Speed

Bold marks the fastest in each column.

One page at a time, the engine alone on its GPU or API. Most engines read the same 24 printed pages from the sets above. ‡ Nemotron 3.5 Parse and OpenThai2.0-OCR-Markdown: 24 court-judgment pages (full A4 pages of dense text). † Typhoon OCR V2 2B: the 65 th-docs-tables pages, served with --enforce-eager, which it needs on our vLLM version. § PaddleOCR-VL 1.6: every sample page, one at a time. CPU seconds are one single-threaded worker on an Intel Xeon 8468 core; at 0.504 CPU seconds a page, this model reads about 950 pages a minute on 8 cores. On shorter pages the GPU models are faster: OpenThai2.0-OCR-Markdown's own card gives 1.6 s a page, the median of 12 table pages.

Use

The guide above walks through installation on Windows, Mac and Linux, with a script for pictures and PDFs: English · ภาษาไทย · 中文. The minimal use:

from runtime.line_engine import LineOCR      # needs openvino (or onnxruntime), opencv-python-headless, pyclipper, numpy

ocr = LineOCR(".", threads=1)                # this folder: det.onnx, rec.onnx, rec_dict.txt, config.json
r = ocr.read("page.png")                     # path, bytes, BGR ndarray or PIL image
print(r["text"])                             # lines in reading order (columns one after another)
r["lines"]                                   # [{"text", "confidence", "row", "box"}]; boxes in the input image's coordinates
r["rotation"], r["text_angle"]               # quarter turns applied (0/90/180/270) and the page's text angle in degrees

For throughput, run one worker process per core with threads=1. Threads in one process stop scaling at about 2 cores, because the Python parts hold the GIL.

What the runtime handles

• Page orientation from the text itself: sideways and upside-down pages are turned the way their lines read best.
• Deskew above 2.5°, then a second detection; boxes come back in the caller's coordinates.
• Reading order: two- and three-column pages are read column by column; tables, budget pages and lists numbered across the row stay in row order.
• Colour: each column's background is subtracted, so light-on-dark text, coloured slides and gradients read.
• Clean-up: detached-mark fragments are dropped, lines merged across a gutter are split, and Thai/Latin look-alikes are fixed (a lone W glued to Thai becomes พ).

Robustness suite: 22 generated conditions (rotation, tilt, huge and tiny text, colour, faded print, photocopy noise, phone photos, tables, columns), 20 held-out pages each, bars fixed before the first run. All 22 pass.

Files

Training

The recognizer starts from the PP-OCRv6-tiny recognizer (Apache-2.0) with a new Thai output layer, fine-tuned with CTC on:

• synthetic Thai/English lines rendered on the fly from 85 OFL/TLWG fonts plus 29 bold weights, with court-text, dictionary, numeric and field strings, English from wikitext, short labels and cross-script look-alike drills;
• 261,877 Administrative Court scan lines, labelled with the court's own DOC text;
• 41,101 parliament budget-PDF lines, text-layer labels kept only where the recognizer independently agrees;
• 107,292 Wikisource old-book lines (level-3 pages; the level-4 pages are a test set).

No OneOCR output and no Gemini labels were used, no personal-data sets, and every benchmark document is excluded by document.

Acknowledgements

The recognizer was trained on NVIDIA H100 GPUs provided by Siam AI. The detector and the recognizer's starting point are PaddlePaddle's PP-OCRv6-tiny.

Limitations

• Printed text only. Handwriting and scene text are weak (see the table); use a VLM for those.
• Decorative display fonts: short labels in heavily stylised fonts can be misread.
• Stamps and seals come out as text, as they are text on the page.
• Court judgments are in-domain: court scans (other documents than the 150 tested) are in the training data.
• The bf16 speed needs AMX (Sapphire Rapids or later); OpenVINO falls back to fp32 on other CPUs.

PP-OCRv6 models © PaddlePaddle authors, Apache-2.0; see NOTICE.