Exploring Font-independent Features for Scene Text Recognition
Scene text recognition (STR) has been extensively studied in last few years. Many recently-proposed methods are specially designed to accommodate the arbitrary shape, layout and orientation of scene texts, but ignoring that various font (or writing) styles also pose severe challenges to STR. These methods, where font features and content features of characters are tangled, perform poorly in text recognition on scene images with texts in novel font styles. To address this problem, we explore font-independent features of scene texts via attentional generation of glyphs in a large number of font styles. Specifically, we introduce trainable font embeddings to shape the font styles of generated glyphs, with the image feature of scene text only representing its essential patterns. The generation process is directed by the spatial attention mechanism, which effectively copes with irregular texts and generates higher-quality glyphs than existing image-to-image translation methods. Experiments conducted on several STR benchmarks demonstrate the superiority of our method compared to the state of the art.
Code (0)
등록된 구현이 없습니다.
Tasks
Image-to-Image TranslationScene Text RecognitionTranslationSimilar Papers 제목 키워드 기반
Towards Boosting the Accuracy of Non-Latin Scene Text Recognition
Scene-text recognition is remarkably better in Latin languages than the non-Latin languages due to several factors like multiple fonts, simplistic vocabulary statistics, updated data generation tools, and writing systems…
Scene Text RecognitionDeformation Robust Text Spotting with Geometric Prior
The goal of text spotting is to perform text detection and recognition in an end-to-end manner. Although the diversity of luminosity and orientation in scene texts has been widely studied, the font diversity and shape va…
DiversityText DetectionText SpottingUnsupervised Landmark DetectionWeakly Supervised Scene Text Generation for Low-resource Languages
A large number of annotated training images is crucial for training successful scene text recognition models. However, collecting sufficient datasets can be a labor-intensive and costly process, particularly for low-reso…
Scene Text RecognitionText GenerationLarge Scale Font Independent Urdu Text Recognition System
OCR algorithms have received a significant improvement in performance recently, mainly due to the increase in the capabilities of artificial intelligence algorithms. However, this advancement is not evenly distributed ov…
Incremental LearningOptical Character Recognition (OCR)Transfer Learning for Scene Text Recognition in Indian Languages
Scene text recognition in low-resource Indian languages is challenging because of complexities like multiple scripts, fonts, text size, and orientations. In this work, we investigate the power of transfer learning for al…
Scene Text RecognitionTransfer Learning