本論文研究的主題是以深度學習為主的文字辨識技術，著重在利用深度學習，將類神經網路演算法加以應用，而在深度學習中，文字辨識將印刷或手寫的文字轉換成電腦可判別理解的格式，以便檢索或分析。 當深度學習應用於文字辨識時，會使用BERT (Bidirectional Encoder Representations from Transformers)模型及LSTM (Long Short-Term Memory)模型，這是一個對於自然語言處理(NLP)領域中很適合的訓練模型，本論文的整個流程，從準備資料集並下載載入到模型，接著將文字資料轉換成模型可接受的格式，其中可能會包含文字中的單詞或子詞，接著建立深度學習模型架構，使用TensorFlow或PyTorch深度學習框架來進行模型訓練，定義損失函數、優化器和訓練的迭代次數，最後獲得經由以上AI模型校正後，衡量指標可能包括準確度、精確度，當所有的模型評估完成後，再將模型部屬到應用中，可能會將模型包裝成API服務，整合到網站或是應用程式中。

The artificial intelligence has been used this generation, not just in technology but in other different industries. Most companies have also begun to use automation, machine learning algorithms. Artificial intelligence will become the future trend. The research topic of this thesis is deep learning and optical character recognition. This thesis uses artificial neural network to complete the writing. In the process of deep learning research, optical character recognition converts copy or handwriting text into a format that computers can discern and understand. When I use deep learning for character recognition applications, I use the BERT model to train it. I also use TensorFlow or PyTorch deep learning framework for model training. I will use deep learning to calibrate the AI model, and I deploy the results to the application.