Ways to solve the accuracy of microsoft window recognition
Omni-Bot-SDK-OSS relies on YOLO model and OCR technology for WeChat window recognition and message parsing. If the recognition accuracy is insufficient, the following steps can be taken to optimize it:
- Ensure microsoft window visibility: Place the WeChat client in the foreground, avoid overlapping or minimized windows, and maintain a resolution of 1920 x 1080 or higher.
- Adjustment of model parameters: in
config.yamlModify the confidence threshold of the YOLO model (0.7-0.9 is recommended) and the recognition area parameters of the OCR in the - Use of unique identifiers: Add note names to contacts to avoid group chat/contacts with the same name interference, and specify note names instead of nicknames when sending messages.
- Stand-alone equipment deployment: Running the framework on a dedicated device prevents other processes from hogging mouse/keyboard resources.
If the problem persists, go through the following advanced program:
- Manually annotate microsoft window elements in the visualization client to generate customized recognition templates
- Self-trained YOLO model (need to prepare WeChat interface screenshot dataset)
- Adjust OCR preprocessing parameters such as binarization threshold, text area cropping ratio, etc.
This answer comes from the articleOmni-Bot-SDK-OSS: A Visual Recognition-based Automation Framework for WeChat RPAThe
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