xby-ocr-captcha: DeepSeek Harness CAPTCHA Recognition Plugin
`xby-ocr-captcha` is a plugin for DeepSeek Harness (DSH) that automatically recognizes captcha images. It calls an OCR model to convert images into text and is available under the MIT license. Installation is required through the `dsh` command, and an API key must be obtained from the Xiaoben Yang official website. It provides three interfaces: `ocr_captcha` (image URL), `ocr_captcha_for_data_base64` (Base64-encoded data), and `ocr_captcha_for_data_file` (local file path), making it easy for developers to integrate into agent workflows.
Read Morexby-captcha: A Captcha Recognition Toolkit
xby-captcha is a DeepSeek Harness plugin designed to automate the recognition of various captchas by encapsulating external model interfaces. It supports multiple verification types, including text, slider, rotation, icon detection, and math problems, and provides a standardized API for file or Base64 input. This plugin simplifies captcha handling in agent development. Users can install it via the official command and are advised to use it only in a legal and compliant manner.
Read MoreNotes on "My Learning Journey with PaddlePaddle" – End-to-End Recognition of Verification Codes
This article provides a detailed introduction to the process of license plate recognition using PaddlePaddle, covering installation of the environment, reading the dataset, building the model, as well as training and testing. Below are summaries of several key points from the article: ### 1. Environment Setup The author first created a virtual environment for PaddlePaddle and configured the CUDA/CUDNN version. ### 2. Dataset Preparation A dataset containing a large number of license plate images was used. These data are publicly available on GitHub, and each license plate has a corresponding label. The author parsed the files
Read MoreNotes on My PaddlePaddle Learning Journey V——Captcha Recognition
Your tutorial provides a detailed introduction to using PaddlePaddle for captcha recognition, covering steps from dataset preparation, model design to final training and prediction. This series of steps is highly suitable for understanding and learning the basic processes and techniques of deep learning, especially its applications in the field of OCR (Optical Character Recognition). ### Code Structure Analysis 1. **Data Preprocessing**: - The `read_file` function is used to read image files and convert them into a format suitable for model input. - `load_and_tr
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