SmartIdOCR: Automatic Detection and Recognition of Identity card number using Deep Networks

Abstract

Identity authentication is much needed and required in this digital age where the information can be utilized in many areas like banking, finance, insurance, education, etc. The long time in the manual authentication process is tiresome for both sides due to the exchange of data. The challenge lies in verification and information extraction from the ID card during the authentication process. There is an AI-based solution needed to reduce the authentication time. This paper aims to solve this problem by doing real-time authentication of identity cards like PAN and UIDAI using AI techniques with good accuracy. Real-time authentication is done by text detection and text recognition. The text detection is done using a differentiable binarization algorithm. We do not have a real annotated dataset for an ID number. We generated approximately 90000 identity number images synthetically with noise and blur using two fonts. This dataset is divided into training, validation, and testing sets. We present a neural encoder-decoder model with attention for converting ID number line images into editable text. Our method is evaluated based on the text output of the line image. An attention-based approach can tackle this problem in a better way in comparison to other neural techniques using CTC-based models. This paper describes the usage of OpenNMT architecture for recognition due to the flexibility of hyperparameter tuning. We evaluated the text recognition performance on scanned as well as the camera-captured identity card number images. We also compared the current recognition performance of OpenNMT with Tesseract (LSTM) on the same testbed containing 36000 images containing ID numbers only. The proposed approach outperformed the Tesseract in ID number recognition.