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Search Results (24)
  • Open Access

    ARTICLE

    Enhanced Attention-Based Encoder-Decoder Framework for Text Recognition

    S. Prabu, K. Joseph Abraham Sundar*

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2071-2086, 2023, DOI:10.32604/iasc.2023.029105

    Abstract Recognizing irregular text in natural images is a challenging task in computer vision. The existing approaches still face difficulties in recognizing irregular text because of its diverse shapes. In this paper, we propose a simple yet powerful irregular text recognition framework based on an encoder-decoder architecture. The proposed framework is divided into four main modules. Firstly, in the image transformation module, a Thin Plate Spline (TPS) transformation is employed to transform the irregular text image into a readable text image. Secondly, we propose a novel Spatial Attention Module (SAM) to compel the model to concentrate… More >

  • Open Access

    ARTICLE

    Recognition of Urdu Handwritten Alphabet Using Convolutional Neural Network (CNN)

    Gulzar Ahmed1, Tahir Alyas2, Muhammad Waseem Iqbal3,*, Muhammad Usman Ashraf4, Ahmed Mohammed Alghamdi5, Adel A. Bahaddad6, Khalid Ali Almarhabi7

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2967-2984, 2022, DOI:10.32604/cmc.2022.029314

    Abstract Handwritten character recognition systems are used in every field of life nowadays, including shopping malls, banks, educational institutes, etc. Urdu is the national language of Pakistan, and it is the fourth spoken language in the world. However, it is still challenging to recognize Urdu handwritten characters owing to their cursive nature. Our paper presents a Convolutional Neural Networks (CNN) model to recognize Urdu handwritten alphabet recognition (UHAR) offline and online characters. Our research contributes an Urdu handwritten dataset (aka UHDS) to empower future works in this field. For offline systems, optical readers are used for… More >

  • Open Access

    ARTICLE

    Menu Text Recognition of Few-shot Learning

    Xiaoyu1,2, Tian Zhenzhen2, Xin Zihao2, Liu Suolan2, Chen Fuhua3, Wang Hongyuan2,*

    Journal of New Media, Vol.4, No.3, pp. 137-143, 2022, DOI:10.32604/jnm.2022.027890

    Abstract Recent advances in OCR show that end-to-end (E2E) training pipelines including detection and identification can achieve the best results. However, many existing methods usually focus on case insensitive English characters. In this paper, we apply an E2E approach, the multiplex multilingual mask TextSpotter, which performs script recognition at the word level and uses different recognition headers to process different scripts while maintaining uniform loss, thus optimizing script recognition and multiple recognition headers simultaneously. Experiments show that this method is superior to the single-head model with similar number of parameters in end-to-end identification tasks. More >

  • Open Access

    ARTICLE

    End-to-end Handwritten Chinese Paragraph Text Recognition Using Residual Attention Networks

    Yintong Wang1,2,*, Yingjie Yang2, Haiyan Chen3, Hao Zheng1, Heyou Chang1

    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 371-388, 2022, DOI:10.32604/iasc.2022.027146

    Abstract Handwritten Chinese recognition which involves variant writing style, thousands of character categories and monotonous data mark process is a long-term focus in the field of pattern recognition research. The existing methods are facing huge challenges including the complex structure of character/line-touching, the discriminate ability of similar characters and the labeling of training datasets. To deal with these challenges, an end-to-end residual attention handwritten Chinese paragraph text recognition method is proposed, which uses fully convolutional neural networks as the main structure of feature extraction and employs connectionist temporal classification as a loss function. The novel residual… More >

  • Open Access

    ARTICLE

    CNN and Fuzzy Rules Based Text Detection and Recognition from Natural Scenes

    T. Mithila1,*, R. Arunprakash2, A. Ramachandran3

    Computer Systems Science and Engineering, Vol.42, No.3, pp. 1165-1179, 2022, DOI:10.32604/csse.2022.023308

    Abstract In today’s real world, an important research part in image processing is scene text detection and recognition. Scene text can be in different languages, fonts, sizes, colours, orientations and structures. Moreover, the aspect ratios and layouts of a scene text may differ significantly. All these variations appear assignificant challenges for the detection and recognition algorithms that are considered for the text in natural scenes. In this paper, a new intelligent text detection and recognition method for detectingthe text from natural scenes and forrecognizing the text by applying the newly proposed Conditional Random Field-based fuzzy rules… More >

  • Open Access

    ARTICLE

    Multi-Domain Deep Convolutional Neural Network for Ancient Urdu Text Recognition System

    K. O. Mohammed Aarif1,*, P. Sivakumar2

    Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 275-289, 2022, DOI:10.32604/iasc.2022.022805

    Abstract Deep learning has achieved magnificent success in the field of pattern recognition. In recent years Urdu character recognition system has significantly benefited from the effectiveness of the deep convolutional neural network. Majority of the research on Urdu text recognition are concentrated on formal handwritten and printed Urdu text document. In this paper, we experimented the Challenging issue of text recognition in Urdu ancient literature documents. Due to its cursiveness, complex word formation (ligatures), and context-sensitivity, and inadequate benchmark dataset, recognition of Urdu text from the literature document is very difficult to process compared to the… More >

  • Open Access

    ARTICLE

    An improved CRNN for Vietnamese Identity Card Information Recognition

    Trinh Tan Dat1, Le Tran Anh Dang1,2, Nguyen Nhat Truong1,2, Pham Cung Le Thien Vu1, Vu Ngoc Thanh Sang1, Pham Thi Vuong1, Pham The Bao1,*

    Computer Systems Science and Engineering, Vol.40, No.2, pp. 539-555, 2022, DOI:10.32604/csse.2022.019064

    Abstract This paper proposes an enhancement of an automatic text recognition system for extracting information from the front side of the Vietnamese citizen identity (CID) card. First, we apply Mask-RCNN to segment and align the CID card from the background. Next, we present two approaches to detect the CID card’s text lines using traditional image processing techniques compared to the EAST detector. Finally, we introduce a new end-to-end Convolutional Recurrent Neural Network (CRNN) model based on a combination of Connectionist Temporal Classification (CTC) and attention mechanism for Vietnamese text recognition by jointly train the CTC and… More >

  • Open Access

    ARTICLE

    A Netnographic-Based Semantic Analysis of Tweet Contents for Stress Management

    Jari Jussila1, Eman Alkhammash2,*, Norah Saleh Alghamdi3, Prashanth Madhala4, Mohammad Ayoub Khan5

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1845-1856, 2022, DOI:10.32604/cmc.2022.017284

    Abstract Social media platforms provide new value for markets and research companies. This article explores the use of social media data to enhance customer value propositions. The case study involves a company that develops wearable Internet of Things (IoT) devices and services for stress management. Netnography and semantic annotation for recognizing and categorizing the context of tweets are conducted to gain a better understanding of users’ stress management practices. The aim is to analyze the tweets about stress management practices and to identify the context from the tweets. Thereafter, we map the tweets on pleasure and… More >

  • Open Access

    ARTICLE

    Morphological Feature Aware Multi-CNN Model for Multilingual Text Recognition

    Yujie Zhou1, Jin Liu1,*, Yurong Xie1, Y. Ken Wang2

    Intelligent Automation & Soft Computing, Vol.30, No.2, pp. 715-733, 2021, DOI:10.32604/iasc.2021.020184

    Abstract Text recognition is a crucial and challenging task, which aims at translating a cropped text instance image into a target string sequence. Recently, Convolutional neural networks (CNN) have been widely used in text recognition tasks as it can effectively capture semantic and structural information in text. However, most existing methods are usually based on contextual clues. If only recognize a single character, the accuracy of these approaches can be reduced. For example, it is difficult to distinguish 0 and O in the traditional CNN network because they are very similar in composition and structure. To… More >

  • Open Access

    ARTICLE

    Cyclic Autoencoder for Multimodal Data Alignment Using Custom Datasets

    Zhenyu Tang1, Jin Liu1,*, Chao Yu1, Y. Ken Wang2

    Computer Systems Science and Engineering, Vol.39, No.1, pp. 37-54, 2021, DOI:10.32604/csse.2021.017230

    Abstract The subtitle recognition under multimodal data fusion in this paper aims to recognize text lines from image and audio data. Most existing multimodal fusion methods tend to be associated with pre-fusion as well as post-fusion, which is not reasonable and difficult to interpret. We believe that fusing images and audio before the decision layer, i.e., intermediate fusion, to take advantage of the complementary multimodal data, will benefit text line recognition. To this end, we propose: (i) a novel cyclic autoencoder based on convolutional neural network. The feature dimensions of the two modal data are aligned… More >

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