arXiv Computer Vision
Confidence-Aware Ensemble and Long-Word Refinement for Artistic Text Recognition
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
Hugging Face Trending Papers
Jun 23
Advancing WordArt-Oriented Scene Text Recognition: Datasets and Methods
WordArt (artistic text) features highly customized fonts, textures, and layouts, making WordArt-oriented scene TExt Recognition (WATER) substantially more challenging than general Scene Text Recognition (STR). Existing STR datasets and methods, typically built around regular scene text and fixed-template inputs, struggle to scale to WATER.
arXiv Computer Vision
Oct 15
TextVerifier: Seeing Beyond Recognition to Verify Chinese Text Rendering
arXiv:2610.19551v1 Announce Type: new Abstract: Text-to-image models increasingly generate readable Chinese text that still contains local rendering defects, such as a character with an extra or miss...
arXiv Computer Vision
Sep 2
Can Scene Text Recognition Read Rare Compositions?
The paper reports that scene text recognition models, while achieving 89–97% accuracy on standard benchmarks, perform significantly worse on rare word–trigram combinations, with a 10–18 point drop in accuracy at the rare‑word/rare‑trigram corner across multiple languages and models. Scaling the vision backbone improves overall accuracy but does not alleviate this corner‑specific deficit. The authors identify the autoregressive decoder’s lexical prior as the root cause and show that architectural changes—specifically moving from autoregressive to CTC decoding—yield the largest improvement for these rare compositions.
arXiv Machine Learning
Oct 21
What Not to Erase: Saliency-Constrained Augmentation for Low-Resource Handwritten Character Recognition
The paper investigates saliency-guided data augmentation for low-resource handwritten character recognition, proposing that saliency maps can be used to design augmentation variants that perturb, preserve, or constrain visually informative regions. It introduces the Explainability Consistency Index (ECI) to measure saliency stability under perturbations and compares classical transformations, random lacunae, and saliency-constrained lacunae. Results show that saliency-constrained augmentation can improve recognition and explanation consistency, but the optimal strategy depends on architecture and corpus, suggesting saliency should be used as a control mechanism rather than a fixed augmentation rule.
arXiv Computer Vision
Oct 1
TIQA: Human-Aligned Perceptual Text Quality Assessment in Generated Images
arXiv:2603.07119v3 Announce Type: replace Abstract: Recent text-to-image models have improved global realism, but text rendering remains a persistent failure mode: images may look convincing overall,...
arXiv Computation and Language
Sep 30
From Pixels to Pairs: A Comprehensive Benchmark of LLM-Driven Key-Value Extraction in Noisy Document Settings
The paper introduces a controlled benchmark for evaluating large language models (LLMs) on key‑value pair extraction from documents with varying levels of OCR noise. It tests 136 configurations across five instruction‑tuned open‑weight LLMs, three datasets, and four text‑quality conditions, using deterministic decoding to generate 17,688 document‑level inferences. The study finds that clean‑text performance does not reliably predict real‑world robustness, model rankings can reverse under noisy conditions, and few‑shot demonstrations do not always improve accuracy, highlighting reliability risks in OCR‑to‑LLM pipelines.