A Template-Free Approach to Invoice Digitization Leveraging SmolVLM and Heuristic Extraction

Abstract

Invoices are a difficult task to automatically extract key information because of the variability of invoice layouts and prohibitive cost of manual annotation, though this step is vital to automating financial workflows. This paper introduces a template-free lightweight understanding framework of invoices that works efficiently without depending either on layout-specific principles or highly annotated datasets. In contrast to the traditional systems based on handcrafted templates or computationally expensive models, the suggested solution takes advantage of the vision-language reasoning of SmolVLM and the use of the heuristic-based post-processing in order to identify the necessary fields within a broad variety of invoice templates including invoice number, date, vendor name, VAT, tax, discount, sub-total and total amount, etc. The experimental findings show that the framework is robust in respect to a wide range of multi-layout invoices, providing a viable and scalable solution to the automation by virtue of invoice in the real-world. Transparency and auditability are also highlighted in the proposed method which allows extracted information to be simply validated in compliance-driven domains like banking and finance. In addition, due to its lightweight nature, it has low computational overhead and is hence deployable in both large-scale and even in small to medium-sized businesses with low resource capabilities. The framework integrates semantic reasoning and rule-based validation thus bringing forward the development of explainable and resource-efficient intelligent document processing, placing it as an alternative to template-based or transformer-intensive models in practice.