Androidapplikation för digitalisering av formulär : Minimering av inlärningstid, kostnad och felsannolikhet

Sammanfattning: This study was performed by creating an android application that uses custom object recognition to scan and digitalize a series of checkbox form for example to correct multiple-choice questions or collect forms in a spreadsheet. The purpose with this study was to see which dataset and hardware with the machine learning library TensorFlow was cheapest, price worthy, enough reliable and fastest. A dataset of filled example forms with annotated checkboxes was created and used in the learning process. The model that was used for the object recognition was Single Show MultiBox Detector, MobileNet version, because it can detect multiple objects in the same image as well as it doesn’t have as high hardware requirements making it fitted for phones. The learning process was done in Google Clouds Machine Learning Engine with different image resolutions and cloud configurations. After the learning process on the cloud the finished TensorFlow model was converted to the TensorFlow Lite model that gets used in phones. The TensorFlow Lite model was used in the compilation of the android application so that the object recognition could work. The android application worked and could recognize the inputs in the checkbox form. Different image resolutions and cloud configurations during the learning process gave different results when it comes to which one was fastest and cheapest. In the end the conclusion was that Googles hardware setup STANDARD_1 was 20% faster than BASIC that was 91% cheaper and more price worthy with this dataset.