Design and Development of Mobile Terminal Application Based on Android
DOI:
https://doi.org/10.31449/inf.v47i2.4008Abstract
In order to study the design and development of mobile terminal application based on Android, this paper proposes the design and development of character recognition system application based on Android platform. Firstly, the implementation process of image acquisition module, image clipping module, image preprocessing module, character recognition, recognition history display module and recognition result post-processing module is introduced in detail, and the implementation algorithm is analyzed in detail. Finally, the method of character training by Tesseract is introduced. By using the training tool based on LSTM (long short - term memory) neural network to train the sample set, a specific character training set is obtained, and OCR (optical character recognition) can be used in specific occasions. The function and performance of the system are tested, and the experimental results are analyzed. After the whole OCR system is built and deployed, the operation and completion of the whole OCR system can be understood through the test and analysis of system functions. It can be seen from the test results that the average response time of text images in pure English is the fastest, the response time of text images in pure Chinese is the second, and the slowest is the mixed arrangement of Chinese and English. For pure English text images, the character recognition accuracy is about 90%, and for pure Chinese text images, the recognition accuracy is close to 90%. However, for the mixed arrangement of Chinese and English, the accuracy of character recognition is lower than that of pure Chinese and English. Nowadays, the accuracy of most commercial character recognition software is about more than 90%. Except for the mixed arrangement of Chinese and English, other products can basically achieve the accuracy of general commercial character recognition software. The product has certain practicability and can be applied to recognize text images taken in natural scenes in daily life.
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