Titlebook: Document Analysis Systems; 16th IAPR Internatio Giorgos Sfikas,George Retsinas Conference proceedings 2024 The Editor(s) (if applicable) an

anniversary

妨礙

Leveraging Semantic Segmentation Masks with?Embeddings for?Fine-Grained Form Classificationssification is impractical for large collections due to its labor-intensive and error-prone nature. To address this, we propose a representational learning strategy that integrates semantic segmentation and deep learning models such as ResNet, CLIP, Document Image Transformer (DiT), and masked auto-

胎兒

DocLightDetect: A New Algorithm for?Occlusion Classification in?Identification Documentsin the physical realm raises significant challenges. Several entities, including financial institutions, insurance companies, and government services, require photos of documents sent through mobile applications to associate the physical and digital personas. This procedure entails significant compu

HEPA-filter

Confidence-Aware Document OCR Error Detection utility of OCR confidence scores for enhancing post-OCR error detection. Our study involves analyzing the correlation between confidence scores and error rates across different OCR systems. We develop ConfBERT, a BERT-based model that incorporates OCR confidence scores into token embeddings and off

Grating

破布

oring of handwritten short descriptive answers in Japanese language exams. We used a deep neural network (DNN)-based handwriting recognizer and a transformer-based automatic scorer without correcting misrecognized characters or adding rubric annotations for scoring. We achieved acceptable agreement

Limpid

https://doi.org/10.1007/978-1-349-06578-3n. This technological intervention can help streamline and standardize the decision-making process across all levels of courts. One key benefit of developing such a system is that the junior judges can benefit from the collective knowledge stored in the knowledge base, improving their ability to mak

痛打

o the coexistence of signatures with other textual and graphical elements on real-world documents. Verification systems must first detect the signature and then validate its authenticity, a dual challenge often overlooked by current datasets and methodologies focusing only on verification. To addres

Indelible

choleretic

e-Vision (LV) models for document analysis and predictions on document images, respectively. Usually, deep neural networks for the DocVQA task are trained on datasets lacking instructions. We show that using instruction-following datasets improves performance. We compare performance across document-