Using Neighborhood Context to Improve Information Extraction from Visual Documents Captured on Mobile Phones

Information Extraction from visual documents is useful in practice to enable intelligent assistant to users. We present an approach that combines local context information and contextual language models to improve information extraction accuracy. We show that our method is able to perform well across model sizes and able to work well with small models that can be useful in applications that need efficient processing (e.g., mobile computing). Our method outperformed state-of-the-art global context based technique and our implementation on a mobile platform suggests its usefulness in practical real-world applications.