Titlebook: Document Analysis and Recognition - ICDAR 2024; 18th International C Elisa H. Barney Smith,Marcus Liwicki,Liangrui Peng Conference proceedi

忘恩负义的人

VAN

Font Impression Estimation in the Wildssions and a convolutional neural network (CNN) framework for this task. However, impressions attached to individual fonts are often missing and noisy because of the subjective characteristic of font impression annotation. To realize stable impression estimation even with such a dataset, we propose

肥料

Typographic Text Generation with Off-the-Shelf Diffusion Modelted texts render them insufficient in the realm of typographic design. This paper proposes a typographic text generation system to add and modify text on typographic designs while specifying font styles, colors, and text effects. The proposed system is a novel combination of two off-the-shelf method

动物

Impression-CLIP: Contrastive Shape-Impression Embedding for Fontsression is weak and unstable because impressions are subjective. To capture such weak and unstable cross-modal correlation between font shapes and their impressions, we propose Impression-CLIP, which is a novel machine-learning model based on CLIP (Contrastive Language-Image Pre-training). By using

敲竹杠

hypotension

Script Identification in the Wild with FFT-Multi-grained Mix Attention Transformerfferent scripts. Specifically, scene text-based script identification is challenged by inter-language similarities, complex backgrounds, and diverse text styles. To address the above problem, we use FFT Block to map the token to the frequency domain and decompose it into multiple frequency component

arbiter

SAGHOG: Self-supervised Autoencoder for Generating HOG Features for Writer Retrievalg involves the application of the Segment Anything technique to extract handwriting from various datasets, ending up with about 24k documents, followed by training a vision transformer on reconstructing masked patches of the handwriting. . is then finetuned by appending NetRVLAD as an encoding layer

全国性

Analysis of the Calibration of Handwriting Text Recognition Modelsable when facing new data. In this context, it is essential to correctly estimate an approximate error of the target predictions. To achieve this, the model must be well calibrated, meaning that the confidence values are sufficiently representative of the expected accuracy. Calibration is a crucial

Fierce

mutineer