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FAQ

A. The required device configuration depends on the expected inference latency and usage pattern (CPU-based inference, GPU-based inference, etc.). Below are the system requirements recommended by our company.

Minimum Requirements

Software Requirements

Item Requirement
Supported OS Windows / macOS / Linux

Hardware Requirements

Item Requirement
Memory 8 GB or more
CPU Architecture Intel / ARM
CPU Performance 1 GHz, 4 cores or more
Storage At least 2 GB of free disk space

GPU Requirements

Item Requirement
GPU Requirement Not required (can run on CPU only)
GPU Architecture NVIDIA (if GPU is used)
GPU VRAM 8 GB or more (if GPU is used)

Network Requirements

For offline device-authenticated licenses

Item Requirement
Runtime Not required (can operate in an offline environment)
Initial Setup Network connection required

Software Requirements

Item Requirement
Supported OS Windows / macOS / Linux

Hardware Requirements

Item Requirement
Memory 16 GB or more
CPU Architecture Intel / ARM
CPU Performance 2 GHz, 16 cores or more
Storage At least 2 GB of free disk space

GPU Requirements

Item Requirement
GPU Requirement Recommended
GPU Architecture NVIDIA
GPU VRAM 16 GB or more

Network Requirements

For offline device-authenticated licenses

Item Requirement
Runtime Not required (can operate in an offline environment)
Initial Setup Network connection required

Q. What information is transmitted during online authentication and execution logging?

A. Only the following information is transmitted during communication.

Information Sent During License Authentication

{
    "check_timing": "launch",
    "license_key": "string",
    "machine_fingerprint_hash": "string",
    "secret_key": "string"
}
  • machine_fingerprint_hash is sent only when using a perpetual (one-time purchase) license.
  • Actual device information is not transmitted; only a hashed value is used.

Information Sent in Execution Logs

{
    "attributes": {
        "modules": []
    },
    "count": 0,
    "license_key": "string",
    "timestamp": "2019-08-24T14:15:22Z"
}
  • The contents of processed images or PDF files are not transmitted.
  • Only information required for usage aggregation and license management is sent.

Q. Japanese fonts are not displayed correctly or are not rendered in images when processing PDFs.

A. Japanese fonts may not be installed in your execution environment. Please install commercially usable Japanese fonts such as IPA fonts.

Linux

apt-get update && \
apt-get install -y \
  fonts-ipafont \
  fonts-ipaexfont \
  fonts-noto-cjk

macOS

brew tap homebrew/cask-fonts
brew install --cask font-ipaexfont
brew install --cask font-noto-sans-cjk
brew install --cask font-noto-serif-cjk

Windows

  1. Visit https://moji.or.jp/ipafont/
  2. Extract the ZIP file
  3. Right-click the .ttf files and select Install

Q. How can I check the number of processed pages?

A. You can check usage via the API or the dashboard.

Using the API

query_count \
  --license_key ${YOMITOKU_LICENSE_KEY} \
  --secret_key ${YOMITOKU_SECRET_KEY}

Using the Dashboard

  • Available for subscription licenses (Standard plan or higher).
  • Dashboard accounts are issued separately and will be provided via email.

Q. How do I use the orientation correction module? What should I do if images are rotated sideways?

A. Automatic correction is available using the orientation correction AI.

CLI

Specify the --rotate_detection option.

yomitoku ${path_data} --rotate_detection

Python API

Set enable_preprocess=True.

analyzer = DocumentAnalyzer(enable_preprocess=True)

Q. I would like to use YomiToku as a Web API.

A. YomiToku-Pro includes a built-in REST API server. Deployment via Docker is also supported.

See the Server page for details.

Q. During installation, the following message appears and the package name is shown as UNKNOWN.

Successfully built UNKNOWN
Installing collected packages: UNKNOWN
Successfully installed UNKNOWN-0.0.0

A. The version of build tools such as setuptools may be outdated. Please update them using the following command.

python -m pip install --upgrade pip setuptools>=77 wheel build