而若要在深度學習的方法下得到非常準確以及穩健的結果，則往往需要非常大量的資料作進行網路模型的訓練；另外在深度學習進行訓練以及測試前提下，需要對影像做預處理如：影像的裁切、影像的縮放與轉正、影像的標記以及利用影像處理的方法增加影像的數量等…。然而影像的預處理是一件非常耗費時間與精力的工作，所以為了能夠只需要少量資料，而得到很好的準確率以及穩健性的目標，本篇論文利用了遷移學習的方法。除了在預訓練模型需要耗費大量資料與時間之外，對於再訓練模型的後續應用上，能夠以少量的文字影像資料，使得測試準確度可達到95% 以上的水準。 ;The application of text detection and recognition on optical images is quite extensive. For example, recognition of production date, product part number and drug number, etc... To recognize the text on an image, one has to first detect the bounding box of the text, and then perform the text recognition for the localized image.
However, in order to get a very accurate and robust results under deep learning method, huge amount of data is indispensable for the training of the network model. In addition, before training and testing a deep learning model, it is important to preprocess the image, such as image cropping, scaling and rotating… etc. Data augmentation, which is an approach to increase the number of images, is also important. However, image preprocessing is a very time-consuming and tedious work. In this research, transfer learning is applied to achieve the goal of deep learning training using a small amount of data and get a model with a good accuracy and robustness. In addition to the large amount of data and time required in pre-training a model, the subsequent retrained model can achieve an accuracy higher than 95% in a small amount of text image data.
