This example demonstrates an approach to recognize handwritten japanese text lines using OpenVINO™. This model supports all the characters in datasets Kondate and Nakayosi.
How It Works
The demo expects the following model in the Intermediate Representation (IR) format:
It can be your own models or pre-trained model from OpenVINO Open Model Zoo. In the
models.lst are the list of appropriate models for this demo that can be obtained via
Model downloader. Please see more information about
Model downloader here.
The demo workflow is the following:
The demo first reads an image and performs the preprocessing such as resize and padding. Then after loading model to the plugin, the inference will start. After decoding the returned indexes into characters, the demo will display the predicted text.
Installation and dependencies
The demo depends on:
To install all the required Python modules you can use:
pip install -r requirements.txt
### Command line arguments
usage: handwritten_japanese_recognition_demo.py [-h] -m MODEL -i INPUT
[-d DEVICE] [-ni NUMBER_ITER]
-h, --help Show this help message and exit.
-m MODEL, --model MODEL
Path to an .xml file with a trained model.
-i INPUT, --input INPUT
Required. Path to an image to infer
-d DEVICE, --device DEVICE
Optional. Specify the target device to infer on; CPU,
GPU, FPGA, HDDL, MYRIAD or HETERO: is acceptable. The
sample will look for a suitable plugin for device
specified. Default value is CPU
-ni NUMBER_ITER, --number_iter NUMBER_ITER
Optional. Number of inference iterations
-cl CHARLIST, --charlist CHARLIST
Path to the decoding char list file
python handwritten_japanese_recognition_demo.py -i data/test.png -m path/to/ir_xml/model.xml
The application uses the terminal to show resulting recognition text and inference performance.