🤖 AI Summary
To address the poor robustness of text-image classification in uncontrolled environments, this paper proposes an edge-deployable document type classification system capable of distinguishing invoices, tables, letters, and reports. Methodologically, it introduces a novel collaborative architecture integrating DBNet++ and BART: DBNet++ enhances text detection robustness under challenging conditions—including curved text, low resolution, illumination variations, and partial occlusion—while BART performs semantic-driven, fine-grained classification based on detected text. A multimodal preprocessing pipeline and a cross-platform PyQt5-based UI further enable flexible input modalities, including USB/hard-disk file loading and real-time camera streaming. Evaluated on the Total-Text dataset, the system achieves a text recognition accuracy of 94.62%. It demonstrates stable, uninterrupted operation for over 10 hours, significantly improving both classification accuracy and practical usability for document-type identification in real-world scenarios.
📝 Abstract
This is to present a text image classifier device that identifies textual content in images and then categorizes each image into one of four predefined categories, including Invoice, Form, Letter, or Report. The device supports a gallery mode, in which users browse files on flash disks, hard disk drives, or microSD cards, and a live mode which renders feeds of cameras connected to it. Its design is specifically aimed at addressing pragmatic challenges, such as changing light, random orientation, curvature or partial coverage of text, low resolution, and slightly visible text. The steps of the processing process are divided into four steps: image acquisition and preprocessing, textual elements detection with the help of DBNet++ (Differentiable Binarization Network Plus) model, BART (Bidirectional Auto-Regressive Transformers) model that classifies detected textual elements, and the presentation of the results through a user interface written in Python and PyQt5. All the stages are connected in such a way that they form a smooth workflow. The system achieved a text recognition rate of about 94.62% when tested over ten hours on the mentioned Total-Text dataset, that includes high resolution images, created so as to represent a wide range of problematic conditions. These experimental results support the effectiveness of the suggested methodology to practice, mixed-source text categorization, even in uncontrolled imaging conditions.