Notice

• Sorry, because limit of platform training credit, if you wanna to try this model or more detail for this project, We recommand you checkout this URL
• https://app.roboflow.com/jackresearch3/high-precision-taiwan-license-plate-number-recognition/overview
• Disadvantages: Lower detection confidence , since I don't have any more time to help with this project, if you would like to help us mark it, please contact me and we will be happy to provide other technical assistance.
• Advantages: Image fine cropped.

This project just for archive usage, you can training you own model

English

The "Taiwan License Plate Character Recognition Research" project focuses on identifying characters primarily based on Taiwan license plate fonts, coupled with license plate detection technology. Through our simple yet practical code, users can assemble a full license plate number according to the X-coordinate of the characters. The aim of this project is to optimize the license plate recognition process, enabling a faster, more accurate capture of license plate numbers.

Generate by GPT4 Here are a few use cases for this project:

1. Automated Parking System: Utilize the "taiwan-license-plate-char-recognition-research" model to read and recognize license plates in parking lots, allowing for streamlined and automated entry/exit management and billing.

2. Traffic Surveillance and Enforcement: Integrate the model into traffic monitoring systems to identify traffic violators, such as speeding or running red lights, by capturing and recognizing their license plates, and assist law enforcement in issuing fines or citations.

3. Stolen Vehicle Detection: Leverage the model within police and security systems to identify stolen or flagged vehicles by matching their license plates in real-time with a database of reported stolen or wanted vehicles.

4. Intelligent Transportation System: Incorporate the model into smart city infrastructure for monitoring and predicting traffic flow, analyzing road conditions, and managing traffic signals, based on real-time vehicle count and license-plate identification.

5. Access Control and Security: Implement the model in gated communities, corporate campuses, or sensitive facilities to provide automated access control to authorized vehicles, enhancing security and convenience for residents, employees, and visitors.

Additional Explanation: The images in this project come from multiple different authors' projects. Prior to the creation of this dataset, we performed the following steps on the images:

1. Detected the presence of license plates.
2. Centered and cropped the images around the license plates to reduce training time and maintain the proportion of license plate fonts.
3. Manually inspected the clarity of the license plate letters.
4. Completed preliminary annotations.
5. Conducted semi-automatic annotations using a smaller initial model.
6. Reviewed each image individually.

However, due to the complexity of annotating license plate fonts and the high requirements for image quality, we have only annotated over 700 images with our limited manpower. Notably, each image requires at least seven letters to be marked.

If you have other questions or want to discuss this data set, you can contact: https://t.me/jtx257

注意事項

• 很抱歉，由於平台培訓點數的限制，如果您想嘗試這個模型或了解此項目的更多細節，我們建議您查看以下URL:
• https://app.roboflow.com/jackresearch3/high-precision-taiwan-license-plate-number-recognition/overview
• 缺點：檢測信心較低。由於我沒有更多時間幫助這個項目，如果您願意幫助我們進行標記，請聯絡我(https://t.me/jtx257)，我們將樂於提供其他技術協助。
• 優勢：圖像精細裁剪。

這個項目僅用於存檔使用，您可以訓練自己的模型

台灣車牌字元識別研究

英文

台灣車牌字元識別研究專案主要聚焦於識別基於台灣車牌字體的字元，結合車牌檢測技術。通過我們簡潔實用的程式碼，用戶可以根據字元的X坐標組合出完整的車牌號碼。此項目旨在優化車牌識別過程，使其更快速、準確地捕捉車牌號碼。

由GPT4生成 以下是此項目的幾個應用案例：

1. 自動停車系統：利用“台灣車牌字元識別研究”模型，在停車場讀取和識別車牌，從而實現出入口管理和計費的自動化。

2. 交通監控與執法：將模型整合到交通監控系統中，識別違反交通規則的行為，如超速或闖紅燈，通過捕捉並識別其車牌，協助執法部門開出罰單或傳票。

3. 被盜車輛檢測：在警方和安全系統中利用該模型，通過與報告中被盜或通緝車輛的數據庫即時匹配其車牌，識別被盜或被標記的車輛。

4. 智能交通系統：將模型納入智慧城市基礎設施，基於實時車輛計數和車牌識別，用於監測和預測交通流量，分析道路條件，並管理交通信號。

5. 出入控制與安全：在封閉社區、企業園區或敏感設施中實施該模型，為授權車輛提供自動出入控制，提升居民、員工和訪客的安全性和便利性。

額外說明: 該專案的圖片來自多個不同作者的專案。在製作這個資料集之前，我們已經對照片進行了以下幾個步驟:

1. 檢測車牌存在。
2. 以車牌為中心點，切割圖片，旨在降低訓練時間，並保留車牌字型的比例。
3. 進行人眼檢查，確認車牌字母是否清晰。
4. 進行初步的標註。
5. 利用較小的初始模型進行半自動標註。
6. 對每一張照片進行逐一檢查。

然而，由於車牌字型的標註難度較大，並且對照片品質要求嚴格，我們在有限的人力下，僅標註了700多張照片。值得注意的是，每張照片至少有7個字母需要進行標記。

如果對此資料集有其他疑問或想討論的，可聯繫: https://t.me/jtx257