臺灣博碩士論文加值系統

字元辨識是一種利用圖像處理和機器學習技術來識別文本中的字元資料的技術。近年來，隨著人工智慧、物聯網、車聯網等技術的發展，對字元辨識系統的需求越來越高。相關的應用的領域包含文本識別、自動控制、車牌辨識等。
在本論文中，透過現場可程式化邏輯陣列(Field Programmable Gate Array , FPGA)為基礎之平台，結合顯示器、鏡頭裝置探討一種字元辨識系統。我們提出兩種字元辨識的相關應用，應用一是透過二值化、連通元件標記方法與樣板匹配實現識別發票上的流水號，應用二是將鏡頭獲取的圖像轉換為灰階圖像，以索伯計算獲取圖像中的車牌資訊，實現車牌定位的功能，在鎖定的車牌區間內以元件標記算法將字元切割，結合樣板匹配實現字元辨識，以實現車牌辨識之功能。

Character recognition is a technology that uses image processing and machine learning techniques to identify character data in text. In recent years, with the development of technologies such as artificial intelligence, the Internet of Things, and connected vehicles, the demand for character recognition systems has increased. Relevant application areas include text recognition, automatic control, and license plate recognition.
In this paper, we explore a character recognition system using a platform based on Field Programmable Gate Array (FPGA), combined with a display and camera device. We propose two related applications of character recognition. The first application recognizes the serial numbers on invoices using binarization, object labeling algorithms, and template matching. The second application involves converting images obtained from the camera into grayscale images, using Sobel calculations to obtain license plate information, locating the license plate in a designated area using object labeling algorithms, cutting the characters, and combining template matching to achieve character recognition, thereby realizing license plate recognition.

摘 要 IV
ABSTRACT V
致謝 VI
目錄 VII
圖目錄 IX
表目錄 XI
第一章 緒論 1
1.1 前言 1
1.2 論文動機 2
1.3 論文架構 3
第二章 現場可程式化邏輯陣列介紹 4
2.1 現場可程式化邏輯陣列基礎介紹 4
2.2 現場可程式化邏輯陣列平台選擇 6
2.2.1 Xilinx Nexys Video[13] 6
2.2.2 Altera DE0[14] 7
2.2.3 Altera DE10-Nano Kit[15] 7
2.2.4 開發平台之選擇 8
第三章 常用的字元辨識方法之介紹 9
3.1 字元辨識介紹 9
3.2 卷積神經網路技術[23] 9
3.3 支持向量機分類器[24] 10
3.4 K-mean分群[27] 11
3.5 樣板匹配[28] 12
第四章 字元辨識系統 13
4.1 字元辨識系統各模組說明 13
4.1.1 圖像處理模組 14
4.1.2 連通元件標籤化模組 16
4.1.3 樣板匹配模組 19
4.2 應用於發票辨識系統 22
4.2.1 發票辨識系統之系統流程圖 22
4.2.2 發票辨識系統之系統架構 23
4.2.3 發票辨識系統之數據訊號 25
4.2.4 二值化模組 26
4.2.5 流水號定位模組 27
4.2.6 影像輸出模組 28
4.3 應用於車牌辨識系統 29
4.3.1 車牌辨識系統之系統流程圖 29
4.3.2 車牌辨識系統之系統架構 30
4.3.3 車牌辨識系統之數據訊號 32
4.3.4 灰階轉換模組 33
4.3.5 索伯運算模組 34
4.3.6 車牌定位模組 35
4.4 字元辨識系統實作結果 36
4.4.1 發票辨識之實作 36
4.4.2 車牌辨識之實作 37
第五章 結論與未來研究方向 40
5.1 結論 40
5.2 未來研究方向 40
參考文獻 41

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