COMPARATIVE ANALYSIS OF OBJECT CLASSIFICATION ALGORITHMS: TRADITIONAL IMAGE PROCESSING VERSUS ARTIFICIAL INTELLIGENCE – BASED APPROACH

In the current era of advanced digital technologies, form recognition is integrated into numerous applications, from computer vision to industrial automation. This paper focuses on a comparative analysis of two distinct form recognition algorithms, namely harnessing the power of artificial intelligence (AI) and image processing techniques. The research is motivated by the need to address the trade-off between speed and complexity in form recognition, with a center on real-world applicability. Traditional image processing-based form recognition approaches often require complex coding, substantial domain expertise, and significant computational resources. This complexity can hinder rapid adaptation to changing requirements and the addition of new forms. The aim is to explore whether AI-powered algorithms can offer a more efficient and versatile alternative, reducing the barriers to entry for form recognition tasks. The primary goal of the paper is to compare the performance of AI-based form recognition with image processing-based methods in terms of speed and accuracy. The second goal is to assess the ease of adapting AI-based algorithms to new forms without extensive code changes. Two form recognition algorithms were designed and implemented, one based on artificial intelligence and a second relying on image processing. The AI-powered algorithm uses neural network architecture trained on a predefined dataset of forms. The image processing algorithm employs edge detection and contour analysis techniques

The relationship between IR and multimedia databases

Modern extensible database systems support multimedia data through ADTs. However, because of the problems with multimedia query formulation, this support is not sufficient.\ud \ud Multimedia querying requires an iterative search process involving many different representations of the objects in the database. The support that is needed is very similar to the processes in information retrieval.\ud \ud Based on this observation, we develop the miRRor architecture for multimedia query processing. We design a layered framework based on information retrieval techniques, to provide a usable query interface to the multimedia database.\ud \ud First, we introduce a concept layer to enable reasoning over low-level concepts in the database.\ud \ud Second, we add an evidential reasoning layer as an intermediate between the user and the concept layer.\ud \ud Third, we add the functionality to process the users' relevance feedback.\ud \ud We then adapt the inference network model from text retrieval to an evidential reasoning model for multimedia query processing.\ud \ud We conclude with an outline for implementation of miRRor on top of the Monet extensible database system