Research And Implementation Of Paper Document Image Acquisition And Optimization System Oriented To Text Recognition

With the rapid development of Internet technology and the improvement of the hardware of mobile devices such as smartphones and tablet computers,images collected by shooting documents are widely used in various industries,but the images collected in natural scenes are uneven.It directly affects the use in subsequent scenes and is visually unsightly.Optimize paper document images from various angles,one is to facilitate the storage of paper document images,and the other is to improve the accuracy of document recognition.The paper document image acquisition optimization system is to improve the document image quality.By analyzing the problems in the image shooting of paper documents,this system designs and implements multiple image optimization methods,namely intelligent shooting,deformation correction,and image denoising.The collection optimization of the three modules.In terms of human-computer interaction,the system uses the core function of image optimization to optimize single and batch multiple images on the mobile client.The system is developed in a C/S way.The intelligent shooting module is implemented on a mobile terminal to collect document images,uploaded to the back-end server via HTTP protocol,and the back-end server asynchronously invokes the image acquisition optimization toolkit for optimization.The front-end part in this article will be implemented on the iOS platform.The paper introduces the design and implementation of the paper document image acquisition optimization system in detail.First,the functional requirements are analyzed according to the factors that affect the accuracy of document image recognition,and the overall architecture of the system is designed.Then researched the relevant technology of the development of the system,determined the technical realization of each functional module,and then further analyzed the processing flow of each functional module,completed the work of code writing,which also introduced the self-made document image data set for Network model training and testing of various optimization methods.Finally,the system is tested according to the functional requirements,the results are analyzed and the system is optimized.