Singer is a service that transcribes handwritten and matrix-printed form entries into machine-readable text objects to be used by other applications. Singer uses artificial intelligence (AI) in combination with advanced image-processing techniques.
Singer uses pre-trained generic AI models and learns from the specific domain business to improve its quality for each specific business domain. Instead of the pre-trained generic models, Singer can also be kick-started based on historic labelled data.
Singer’s learning capabilities are based on feedback techniques and the human-in-the-loop principle.
Singer focuses on short pieces of information, such as codes, dates, numbers and domain-specific words. It is not intended to read entire paragraphs of free text. Singer also detects the presence of specific objects, such as stamps or signatures, and whether fields are filled in or not.
Due to the focus on application-form entries it is extremely well suited as an element in the digitalization chain of automatic form processing. At first, Singer examines the regions of interest (ROI) of the form and applies advanced image-processing techniques on these. This results in a number of small image segments. As a next step, each image segment is sent to the value-detection algorithm, which responds with the interpreted values. In some cases a multistep approach is needed if the algorithm to be used depends on the outcome of a value. The algorithms used and steps are easily configured.
Singer is a learning AI solution and thus it needs to be retrained regularly. New patterns will pass through the system, and through the feedback system, these new patterns and labels will be ingested into the Singer learning algorithm. The feedback system allows clear monitoring of the AI quality.
It is advised that the customer provides a labelled historic data set for training purposes. This way, the solution will be performing at a higher level at the beginning of the project. If this cannot be done, the AI learns from scratch or a general model. In the end, Singer will be equally efficient but it will take longer since it has to learn everything from production activities.