1,936 research outputs found
Stroke Extraction of Chinese Character Based on Deep Structure Deformable Image Registration
Stroke extraction of Chinese characters plays an important role in the field
of character recognition and generation. The most existing character stroke
extraction methods focus on image morphological features. These methods usually
lead to errors of cross strokes extraction and stroke matching due to rarely
using stroke semantics and prior information. In this paper, we propose a deep
learning-based character stroke extraction method that takes semantic features
and prior information of strokes into consideration. This method consists of
three parts: image registration-based stroke registration that establishes the
rough registration of the reference strokes and the target as prior
information; image semantic segmentation-based stroke segmentation that
preliminarily separates target strokes into seven categories; and
high-precision extraction of single strokes. In the stroke registration, we
propose a structure deformable image registration network to achieve
structure-deformable transformation while maintaining the stable morphology of
single strokes for character images with complex structures. In order to verify
the effectiveness of the method, we construct two datasets respectively for
calligraphy characters and regular handwriting characters. The experimental
results show that our method strongly outperforms the baselines. Code is
available at https://github.com/MengLi-l1/StrokeExtraction.Comment: 10 pages, 8 figures, published to AAAI-23 (oral
Advances in Character Recognition
This book presents advances in character recognition, and it consists of 12 chapters that cover wide range of topics on different aspects of character recognition. Hopefully, this book will serve as a reference source for academic research, for professionals working in the character recognition field and for all interested in the subject
Recognition techniques for online Arabic handwriting recognition systems
Online recognition of Arabic handwritten text has been an on-going research problem for many years. Generally,
online text recognition field has been gaining more interest
lately due to the increasing popularity of hand-held computers, digital notebooks and advanced cellular phones. However, different techniques have been used to build several online handwritten recognition systems for Arabic text, such as Neural Networks, Hidden Markov Model, Template Matching and others. Most of the researches on online text recognition have divided the recognition system into these three main phases which are preprocessing phase, feature extraction phase and recognition phase which considers as the most important phase and the heart of the whole system. This paper presents and compares techniques that have been used to recognize the Arabic handwriting scripts in online recognition systems. Those techniques attempt to recognize Arabic handwritten words, characters, digits or strokes. The structure and strategy of those reviewed techniques are explained in this article. The strengths and weaknesses of using these techniques will also be discussed
Handwritten Chinese character recognition using spatial Gabor filters and self-organizing feature maps
So far the bottleneck of Chinese recognition, especially handwritten recognition, still lies in the effectiveness of feature-extraction to cater for various distortions and position shifting. In the paper, a novel method is proposed by applying a set of Gabor spatial filters with different directions and spatial frequencies to character images, in an effort to reach the optimum trade-off between feature stability and feature localization. While a classic self-organizing map is used for unsupervised clustering feature codes, a multi-staged LVQ with a fuzzy judgement unit is applied for the final recognition on the feature mapping result.published_or_final_versio
Recent Trends and Techniques in Text Detection and Text Localization in a Natural Scene: A Survey
Text information extraction from natural scene images is a rising area of research. Since text in natural scene images generally carries valuable details, detecting and recognizing scene text has been deemed essential for a variety of advanced computer vision applications. There has been a lot of effort put into extracting text regions from scene text images in an effective and reliable manner. As most text recognition applications have high demand of robust algorithms for detecting and localizing texts from a given scene text image, so the researchers mainly focus on the two important stages text detection and text localization. This paper provides a review of various techniques of text detection and text localization
Adaptive pattern recognition of hand-written Chinese characters using Fourier transforms of projection profiles
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