Text and Facial Features Recognition using Machine Intelligence

We all live in a world and we all are dependent on mobiles especially android for our work. In the era of mobile development, android developers need to build the latest android applications and be in pace with the quickly developing mobile world and to meet the business needs. For meeting worldwide business needs, android applications need to include machine learning methods to make them more effective. To incorporate machine learning techniques into android applications requires a great deal of examination, exploration, and mastery. There are different methodologies for implementing machine learning into versatile applications. One of the proficient ways is to develop an application programming interface (API) utilizing firebase which gives adaptability to the designers to import machine learning models. Google firebase gives a lot more inbuilt tensorflow lite, machine learning models that can be added utilizing the given dependencies. Thus, here we present a methodology for developing an application that extracts text from images and recognizes facial features from photos by utilizing firebase ML Kit and TESTLAB. We can save the written data on images at one place while extracting text with the help of our application. The application shows the facial features of human face on clicking photo or just by uploading any photo. It shows the coordinates of eyes, nose, mouth, eyebrows, recognizes the face shape and provide expression of the face whether the person is happy, sad, angry, laughing etc all with the help of ML Kit.It is a mobile Software Development Kit (SDK) that brings Google’s AI ability to android and iOS applications in an amazing way and simple to utilize library. Regardless of whether you are new or experienced in AI, you can execute the activities you want in a clear and simple way. Then again, assuming you are an accomplished machine learning engineer, ML Kit gives advantageous APIs that assist you with utilizing your custom tensorflow lite models in your portable applications.