# DocParseNet: Advanced Semantic Segmentation and OCR Embeddings for Efficient Scanned Document Annotation Source: [DocParseNet: Advanced Semantic Segmentation and OCR Embeddings for Efficient Scanned Document Annotation]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:36:01.778Z Authors: Ahmad Mohammadshirazi, Ali Nosrati Firoozsalari, Mengxi Zhou, Dheeraj Kulshrestha, Rajiv Ramnath Published: 2024-06-25T14:32:31.000Z ## Abstract Automating the annotation of scanned documents is challenging, requiring a balance between computational efficiency and accuracy. DocParseNet addresses this by combining deep learning and multi-modal learning to process both text and visual data. This model goes beyond traditional OCR and semantic segmentation, capturing the interplay between text and images to preserve contextual nuances in complex document structures. Our evaluations show that DocParseNet significantly outperforms conventional models, achieving mIoU scores of 49.12 on validation and 49.78 on the test set. This reflects a 58% accuracy improvement over state-of-the-art baseline models and an 18% gain compared to the UNext baseline. Remarkably, DocParseNet achieves these results with only 2.8 million parameters, reducing the model size by approximately 25 times and speeding up training by 5 times compared to other models. These metrics, coupled with a computational efficiency of 0.039 TFLOPs (BS=1), highlight DocParseNet's high performance in document annotation. The model's adaptability and scalability make it well-suited for real-world corporate document processing applications. The code is available at https://github.com/ahmad-shirazi/DocParseNet ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()