dsh-tesseract-ocr：为文本模型接入本地图片 OCR
`dsh-tesseract-ocr` 是一款 DeepSeek Harness (DSH) 插件，旨在解决文本模型无法处理图片输入的问题。该插件通过本地调用 Tesseract OCR 引擎识别附件图片中的文字，并将识别后的纯文本发送至模型 API，默认保留原始图片字节在本地，不上传给模型提供商，从而避免修改模型输入模态或传输敏感数据。 核心功能包括：支持文本模型处理图片附件、本地 OCR 识别、默认仅发送文本、可选的视觉模型直通模式（`passthrough: true`），以及未加载时拒绝图片附件的 fail-closed 安全机制。适用场景为使用 DSH 文本模型且需基于图片文字问答的
Read Morepicturereader: Adding Local Image Recognition Capability to the Pure Text DSH Model
picturereader is a DSH model inference plugin (v3.2.0) maintained by jing-hy. It enables pure-text models to achieve native thumbnails through a visual twin adapter, converting images into structured textual evidence using local tools such as image_scan and image_ocr. It supports three routing modes—privacy, intelligence, and rigor—and an optional external VLM. Additionally, it includes document_to_image for converting documents to images and image_edit for local image editing. It runs entirely locally by default and is licensed under MIT.
Read MoreConnect Image and Audio Capabilities to DeepSeek Harness with dsh-vision-complete
`dsh-vision-complete` is a MIT-licensed community plugin for DeepSeek Harness maintained by Yts1919, which equips plain-text models with capabilities including image understanding, OCR, object detection, video processing, speech recognition and PDF handling. The plugin itself does not include any vision models, and by default calls Qwen via MCP. The alternative `vision.py` can be connected to OpenAI-compatible interfaces. The installation command via the catalog page is `dsh plugin add github:Yts1919/dsh-vision-complete`. For full activation, you also need the Windows installation script, a valid DASHSCOPE_API_KEY, and a restart of Harness.
Read MoreEquip Plain-text DeepSeek Harness Agent with "Eyes" Using dsh-vision-router
### 版本1（正式学术/技术文档译法，适配海外开发者语境）： DSH-Vision-Router is a DSH tool and capability plugin (MIT License, v1.4.4) maintained by ysr666. It treats vision models as its "eyes" and DeepSeek as its "brain", and provides free-key-free anonymous fallback service from OVH by default, along with 11 pixel-level tools including question answering, positioning, cropping, pixel comparison and OCR. This article verifies the installation commands, usage of automatic image recognition model groups and permission precautions in accordance with the community directory and GitHub README. --- ### 版本2（更贴合国内开源项目对外宣传的自然译法）： Maintained by ysr666, DSH-Vision-Router is a DSH utility and capability plugin under MIT License (v1.4.4). It takes vision models as its "eyes" and DeepSeek as its "brain", offers free anonymous fallback service powered by OVH by default, and has built-in 11 pixel-level tools such as Q&A, positioning, cropping, pixel comparison and OCR. This article checks the installation commands, usage of the automatic image recognition model suite and permission notes against the community repository and GitHub README.
Read MoreAdding Visual Capabilities to DeepSeek Harness Plain Text Model Using dsh-vision-toolkit
DSH Vision Toolkit is a DeepSeek Harness interface enhancement plugin maintained by Anionex, which connects agent-vision-toolkit to Web/Headless Profile. After pasting images with a plain-text model, users can switch to the Vision Toolkit variant to complete intent-based image question answering, element positioning, long-screenshot OCR and UI restoration with pixel comparison. The directory installation command is `dsh plugin add github:Anionex/dsh-vision-toolkit`, it is licensed under MIT, and the current npm version is 0.1.20.
Read MoreEnd-to-End Recognition of Captchas Based on PaddlePaddle 2.0
Your code has covered most aspects of the CAPTCHA recognition project, including data processing, model training, and inference. Below are some suggestions for improvements and enhancements to your provided code: ### 1. Data Preprocessing Ensure the image dimensions are consistent (27x72), as this is the input size used during training. ### 2. Model Definition Your `Model` class has already encapsulated the network structure well. You can further optimize it and add more comments to facilitate understanding. ### 3. Training Process During the training process, ensure that when using multi-GPU training,
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