PP-OCRv6 Hanzi Unicode OCR

Single-character OCR for CJK glyph images, built on PaddleOCR PP-OCRv6: one cropped image of one glyph in, one Unicode ideograph out.

• Scope: clean, single-colour, dark-on-light renderings of one character from a computer font — the rare-character images embedded in dictionary data. Handwriting, scans, rubbings and photographs are outside the validated envelope.
• Accuracy: 97–99% on that input, across modern regular-script computer fonts (宋体 / 黑体 / 仿宋 / 楷体). Decorative and calligraphic faces are excluded.
• Coverage: all 101,996 CJK unified ideographs of Unicode 17.0 are classes — Extensions A–J plus the URO block, no gaps.

单字字图识别：输入一张只含一个字的图片，输出该字的 Unicode 码位。面向电脑字体渲染的 黑字白底单字图（最初为 OCR 字典中的生僻字插图而做），完整覆盖 Unicode 17.0 全部 101,996 个中日韩统一表意文字。在上述输入条件下（现代电脑正文字体：宋 / 黑 / 仿宋 / 楷） 准确率 97–99%，花体字、书法字不在其列。与 IDS 模型相互独立，可交叉核验。

The two are independent. Run both and compare: disagreements get surfaced for review instead of being silently accepted.

Files

metadata.json holds the class dictionary (vocab), input shape and preprocessing spec.

Model details

The width axis is dynamic in the graph, but the released configuration always feeds W = 320 zero-padded on the right, which is how the model was trained.

Coverage

vocab lists all 101,997 classes in order: index 0 is the CTC blank, then characters by ascending code point. Compared code point by code point against the official UnicodeData.txt of Unicode 17.0:

Counts are the officially assigned code points; unassigned slots (U+3347A–U+3347F in Ext J, 2 in Ext I) are correctly absent. The 12 compatibility ideographs shipped are exactly those Unicode normalisation maps to themselves (U+FA0E 﨎 U+FA0F 﨏 U+FA11 﨑 U+FA13 﨓 U+FA14 﨔 U+FA1F 﨟 U+FA21 﨡 U+FA23 﨣 U+FA24 﨤 U+FA27 﨧 U+FA28 﨨 U+FA29 﨩); the other 990 normalise to an existing unified ideograph, so the model reports that unified code point rather than duplicating it. No class collapses to another under NFC/NFKC, and nothing outside the CJK ideograph repertoire is in the dictionary — a glyph outside it cannot be produced.

Usage

pip install onnxruntime pillow numpy
python example_usage.py glyph.png

import json
import math

import numpy as np
import onnxruntime as ort
from PIL import Image, ImageDraw, ImageFont

meta = json.load(open("metadata.json", encoding="utf-8"))
vocab = meta["vocab"]
C, H, W = meta["image_shape"]  # [3, 48, 320]

session = ort.InferenceSession("model.onnx", providers=["CPUExecutionProvider"])
input_name = session.get_inputs()[0].name


def prepare(im):
    im = im.convert("RGB")
    iw = min(math.ceil(H * im.width / im.height), W)
    bgr = np.asarray(im)[:, :, ::-1]                       # RGB -> BGR
    bgr = np.asarray(Image.fromarray(bgr).resize((iw, H), Image.BILINEAR))
    plane = bgr.astype(np.float32).transpose(2, 0, 1) / 127.5 - 1
    canvas = np.zeros((C, H, W), np.float32)
    canvas[:, :, :iw] = plane
    return canvas[None]                                    # (1, 3, 48, 320)


def recognise(im):
    probs = session.run(None, {input_name: prepare(im)})[0][0]  # (T, 101997)
    index, scores = probs.argmax(-1), probs.max(-1)
    text, kept, last = [], [], None
    for i, s in zip(index, scores):
        if i != last and i != 0:                           # collapse repeats, drop blank
            text.append(vocab[i])
            kept.append(float(s))
        last = i
    return "".join(text), (sum(kept) / len(kept) if kept else 0.0)


# Round-trip a character through a font, then recognise it back.
char = "明"
font = ImageFont.truetype("C:/Windows/Fonts/simsun.ttc", 200)
box = font.getbbox(char)
img = Image.new("L", (box[2] - box[0] + 20, box[3] - box[1] + 20), 255)
ImageDraw.Draw(img).text((10 - box[0], 10 - box[1]), char, font=font, fill=0)

print(recognise(img))   # ('明', 0.9954...)

Preprocessing must be exact — the model is sensitive to channel order and normalisation: RGB source read as BGR, aspect-preserving resize to height 48, scale to [-1, 1], left-aligned in a (3, 48, 320) zero array with the padding on the right. Decoding is greedy CTC: argmax, collapse repeats, drop the blank index 0. The output is already a per-step distribution, so no extra softmax. The score is the mean of the retained step probabilities — a confidence hint, not a calibrated accuracy.

Limitations

• One character per image. No detection, no segmentation.
• High confidence is not proof. It is a closed-set 101,996-way classifier, so a malformed or out-of-repertoire glyph is still forced onto the nearest known character, sometimes with a high score. Cross-check with the IDS model when correctness matters.
• Coverage stops at Unicode 17.0. Ideographs assigned later will be missing until the model is retrained.

Provenance

• Upstream licence: PaddleOCR is Apache-2.0 — see THIRD_PARTY_NOTICES.md.
• The conversion keeps the original 101,997-class CTC dictionary unchanged. On a sample compared against the source Paddle model, the CPU ONNX outputs are character-identical, with a maximum per-frame probability difference of 8.43×10⁻⁵.