Sold by: MLism Inc.
Deployed on AWS
YomiToku is a proprietary document analysis engine specialized for Japanese. It integrates AI OCR plus layout and table parsing models, accurately structuring vertical text, multi column documents, and complex business forms. It supports a wide range of use cases, including generating data for RAG / search, creating searchable PDFs, and extracting information from table data.
We provide **yomitoku-client** as a client SDK to help you use this product more conveniently.For more details, please refer to the link below:
https://github.com/MLism-Inc/yomitoku-client
For long-term or large-scale use, this product is also available through private offers.Please contact our support team for pricing information.
Overview
YomiToku is a proprietary document analysis engine specialized for Japanese. It integrates AI OCR plus layout and table parsing models, accurately structuring vertical text, multi column documents, and complex business forms. It supports a wide range of use cases, including generating data for RAG / search, creating searchable PDFs, and extracting information from table data.
We provide yomitoku-client as a client SDK to help you use this product more conveniently.For more details, please refer to the link below: https://github.com/MLism-Inc/yomitoku-client
For long-term or large-scale use, this product is also available through private offers.Please contact our support team for pricing information.
Highlights
- Each model is specifically trained for Japanese document images, supporting the recognition of over 7,000 Japanese characters, including vertical text and other layout structures unique to Japanese documents. (It also supports English documents.)
- Equipped with four AI models trained on Japanese datasets: text detection, text recognition, layout analysis, and table structure recognition. All models are independently trained and optimized for Japanese documents, delivering high-precision inference.
- By leveraging layout analysis, table structure parsing, and reading order estimation, it extracts information while preserving the semantic structure of the document layout.
Details
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Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.g5.xlarge Inference (Batch) Recommended | Model inference on the ml.g5.xlarge instance type, batch mode | $10.00 |
ml.g4dn.xlarge Inference (Real-Time) Recommended | Model inference on the ml.g4dn.xlarge instance type, real-time mode | $10.00 |
ml.g4dn.xlarge Inference (Batch) | Model inference on the ml.g4dn.xlarge instance type, batch mode | $10.00 |
ml.g5.xlarge Inference (Real-Time) | Model inference on the ml.g5.xlarge instance type, real-time mode | $10.00 |
ml.g6.xlarge Inference (Real-Time) | Model inference on the ml.g6.xlarge instance type, real-time mode | $10.00 |
ml.g6.xlarge Inference (Batch) | Model inference on the ml.g6.xlarge instance type, batch mode | $10.00 |
ml.c7i.xlarge Inference (Batch) | Model inference on the ml.c7i.xlarge instance type, batch mode | $10.00 |
ml.c7i.2xlarge Inference (Batch) | Model inference on the ml.c7i.2xlarge instance type, batch mode | $10.00 |
ml.c7i.xlarge Inference (Real-Time) | Model inference on the ml.c7i.xlarge instance type, real-time mode | $10.00 |
ml.c7i.2xlarge Inference (Real-Time) | Model inference on the ml.c7i.2xlarge instance type, real-time mode | $10.00 |
Dimensions summary
You pay by the hour for each host the document analysis model runs on. Pricing splits along two lines: instance type and inference mode. You choose from GPU instances (ml.g4dn.xlarge, ml.g5.xlarge, ml.g6.xlarge) and CPU instances (ml.c7i.xlarge, ml.c7i.2xlarge). Each instance runs in either batch mode, for processing large document volumes, or real-time mode, for on-demand requests. Not every instance offers both modes. Your total cost scales with the instance size you pick and how many host-hours you run. You add SageMaker instance charges on top.
Top-of-mind questions for buyers
One host-hour is one hour that a SageMaker endpoint instance runs the document analysis model. Charges accrue for every hour the instance stays active, even without incoming requests. Shutting the endpoint down stops software charges. You control this by deploying and deleting endpoints through the client tool.
Two charges apply at the same time. You pay the per-hour software fee shown in the table for the model. You also pay AWS separately for the underlying SageMaker instance. Both meter the same running hours. Larger instance types and longer run times raise both charges together.
Batch mode fits processing large document volumes in one job, such as directory or S3 bulk analysis. Real-time mode fits on-demand requests where you call the endpoint and get results back immediately. Each mode bills per host-hour. Not every instance type offers both modes, so check the table.
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
- Extended Lightweight Mode support to GPUs
- Improved detection model recognition accuracy
Additional details
Inputs
- Summary
Supported Content Types:
- application/pdf - PDF documents (multi-page supported)
- image/jpeg - JPEG images
- image/png - PNG images
- image/tiff - TIFF images
Request Body:
Send the binary file data directly in the request body with appropriate Content-Type header.
Example Usage:
aws sagemaker-runtime invoke-endpoint --endpoint-name ${ENDPOINT_NAME} --content-type "application/pdf" --body fileb://demo/sample.pdf --region ${AWS_REGION} output.json
Sample Image Source:
The sample images used in this documentation are from 「広報誌 令和 6 年 11 月号」 published by 総務省.
Input MIME type
application/pdf, image/jpeg, image/png, image/tiff
- Input MIME type
- application/pdf, image/jpeg, image/png, image/tiff
https://mlism-marketplace-documents.s3.ap-northeast-1.amazonaws.com/samples/gallery1.jpg
https://mlism-marketplace-documents.s3.ap-northeast-1.amazonaws.com/samples/gallery1.jpg
Support
Vendor support
AWS infrastructure support
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