A Metaverse text recognition model based on character-level contrastive learning

Scene text detection and recognition are vital challenging tasks in computer vision, which are to detect and recognize sequences of texts in natural scenes. Recently, researchers have investigated a lot of state-of-the-art methods to improve the accuracy and efficiency of text detection and recognition. However, there has been little research on text detection and recognition in natural scenes in Vietnam. In this paper, a deep learning-based empirical investigation of Vietnamese scene text detection and recognition is presented. Firstly, four detection models including differentiable binarization network (DBN), pyramid mask text detector (PMTD), pixel aggregation network (PAN), and Fourier contour embedding network (FCEN), are employed to detect text regions from the images. Then, four text recognition models including convolutional recurrent neural network (CRNN), self-attention text recognition network (SATRN), no-recurrence sequence-to-sequence text recognizer (NRTR), and RobustScanner (RS) are also investigated to recognize the texts. Moreover, data augmentation methods are also applied to enrich data for improving the accuracy and enhancing the performance of scene text detection and recognition. To validate the effectiveness of scene text detection and recognition models, the VinText dataset is employed for evaluation. Empirical results show that PMTD and SATRN achieve the highest scores among the others for text detection and recognition, respectively. For knowledge-sharing, our implementation is publicly available at https://github.com/ThorPham/VN_scene_text_detection_recognition.

Scene texts refer to arbitrary text found in images captured by cameras in real-world settings. The tasks of text detection and recognition are critical components of computer vision, with applications spanning scene understanding, information retrieval, robotics, and autonomous driving. Despite significant advancements in deep learning methods, achieving accurate text detection and recognition in complex images remains a formidable challenge for robust real-world applications. Several factors contribute to these challenges. First, the diversity of text shapes, fonts, colors, and styles complicates detection efforts. Second, the myriad combinations of characters, often with unstable attributes, make complete detection difficult, especially when background interruptions obscure character strokes and shapes. Finally, effective coordination of multiple sub-tasks in end-to-end learning is essential for success. This research aimed to tackle these challenges by enhancing text discriminative representation. This study focused on two interconnected problems: Scene Text Recognition (STR), which involves recognizing text from scene images, and Scene Text Detection (STD), which entails simultaneously detecting and recognizing multiple texts within those images. This research focuses on implementing and evaluating the Efficient and Accurate Scene Text Detector (EAST) algorithm for text detection and recognition in natural scene images. The study aims to compare the performance of three prominent Optical Character Recognition (OCR) techniques—TesseractOCR, PaddleOCR, and EasyOCR. The EAST model was applied to a series of sample test images, and the results were visually represented with bounding boxes highlighting the detected text regions. The inference times for each image were recorded, highlighting the algorithm's efficiency, with average times of 0.446, 0.439, and 0.440 seconds for the respective test images. These results indicate that the EAST algorithm is accurate and operates in real-time, making it suitable for applications requiring immediate text recognition.

Scene text recognition (STR) involves reading text from images of natural scenes. The texts in such images come in a wide array of fonts, shapes, and orientations. Therefore, various works often rely on rectification network to rectify text images before passing them to the recognition network. However, rectifying an image that does not require it may create unwanted distortion. This may result in wrong predictions that would have otherwise been correct. In order to alleviate the adverse impact of rectification, a portmanteauing of features is presented. The method is introduced to a transformer-based model through a proposed block matrix initialisation which achieved competitive results. Although transformer has achieved notable success in various fields, areas for improvements with its application in STR were identified in this study. Firstly, vision transformer requires an input image to be resized into fixed height and width before being split into patches. However, it was discovered that certain patch resolutions seem to result in better accuracy for images with particular original aspect ratios. Secondly, the first decoded character generally has lower accuracy. In view of these issues, pure transformer with integrated experts (PTIE) is proposed. PTIE is able to process multiple patch resolutions and decode in both the original and reverse character orders thereby capitalising on the aforementioned areas and achieved state-of-the-art results. Handwritten text recognition (HTR) deals with handwritten text images that come from scanned or photograph documents. Works that employed transformer-based models often train them with additional synthetic data. However, these data are not publicly available. Furthermore, experimentation in this study seems to suggest that transformer trained on real HTR data generalises poorly to unseen data. Therefore, in the scope of real data, a simple transformer model which outperformed related works is presented in this thesis. This is achieved by adopting attention masking which addressed the issue of generalisation as well as introducing various pre-processing methods.

Building robust recognizers for Arabic has always been challenging. We demonstrate the effectiveness of an end-to-end trainable CNN-RNN hybrid architecture in recognizing Arabic text in videos and natural scenes. We outperform previous state-of-the-art on two publicly available video text datasets - ALIF and ACTIV. For the scene text recognition task, we introduce a new Arabic scene text dataset and establish baseline results. For scripts like Arabic, a major challenge in developing robust recognizers is the lack of large quantity of annotated data. We overcome this by synthesising millions of Arabic text images from a large vocabulary of Arabic words and phrases. Our implementation is built on top of the model introduced here [37] which is proven quite effective for English scene text recognition. The model follows a segmentation-free, sequence to sequence transcription approach. The network transcribes a sequence of convolutional features from the input image to a sequence of target labels. This does away with the need for segmenting input image into constituent characters/glyphs, which is often difficult for Arabic script. Further, the ability of RNNs to model contextual dependencies yields superior recognition results.

Text recognition in the wild is a challenging task in the field of computer vision and machine learning. Existing optical character recognition engines cannot perform well in the natural scene. In this context, deep learning models have emerged as a powerful state-of-the-art technique in the classification and recognition process. This study proposes a new Convolutional Neural Network based system for scene text reading. We investigate how to combine the character recognition module followed by the word recognition module to achieve the overall system goal. The first module analyzes characters within multi-scale images by relaying on the power of the convolutional network and the fully connected network for character recognition. The second module relies on the Viterbi search to find the closest word to a given characters sequence. For the sake of more precision, a bigram based linguistic module is applied. The proposed system achieves the state-of-the-art performance on three standard scene text recognition benchmarks: chars74k, ICDAR 2003 and ICDAR 2013. In particular, this performance is proven on both of character and word recognition accuracy as well as speed aspects via a comparative study between different deep learning architectures.

Scene text information extraction plays an important role in many computer vision applications. Most features in existing text extraction algorithms are only applicable to one text extraction stage (text detection or recognition), which significantly weakens the consistency in an end-to-end system, especially for the complex Chinese texts. To tackle this challenging problem, we propose a novel text structure feature extractor based on a text structure component detector (TSCD) layer and residual network for Chinese texts. Inspired by the three-layer Chinese text cognition model of a human, we combine the TSCD layer and the residual network to extract features suitable for both text extraction stages. The specialized modeling for Chinese characters in the TSCD layer simulates the key structure component cognition layer in the psychological model. And the residual mechanism in the residual network simulates the key bidirectional connection among the layers in the psychological model. Through the organic combination of the TSCD layer and the residual network, the extracted features are applicable to both text detection and recognition, as humans do. In evaluation, both text detection and recognition models based on our proposed text structure feature extractor achieve great improvements over baseline CNN models. And an end-to-end Chinese text information extraction system is experimentally designed and evaluated, showing the advantage of the proposed feature extractor as a unified feature extractor.

Text detection and recognition in scene images and videos attract much attention in computer vision recently. However, most existing text detection and recognition methods only focus on static images. In this paper an end-to-end scene text recognition method based on multi frame tracking is proposed for text in videos, in which temporal information is employed to improve performance. First, an end-to-end text recognition method based on a unified deep neural network is used to detect and recognize text in each frame of the input video. Then, multi frame text tracking is employed through associations of texts in current frame and several previous frames to obtain final results. Experiments on ICDAR datasets demonstrate that the proposed method outperforms the state-of-the-art methods in end-to-end video text recognition.

Due to the various individual handwriting styles and diverse structures of Chinese characters, it is rather intricate to recognize handwritten Chinese characters precisely. In this paper, a muti-stage system that combines a Differentiable Binarization Network (DBNet) for text detection and Scene Text Recognition with a Single Visual Model (SVTR) for effective character recognition is developed, aiming to offer a potential solution for correct identification of handwritten Chinese characters in real-world scenarios. More specifically, the proposed system utilized the CASIA-HWDB2.x dataset from the Institute of Automation of the Chinese Academy of Sciences to train text detection and recognition models. For the detection, the Differentiable Binarization method in the Head Network applies a dynamic threshold map for text-background differentiation. On top of the SVTR employed as a recognition module, this research proposed a performance-optimized PP-LCNet architecture and the Guided Training of Continuous Time-Series Classifiers (GTC) with TextConAug, which is a data augmentation strategy to accommodate real-world scenarios. Briefly, DBNet and SVTR are integrated to form an end-to-end system. The output of DBNet after undergoing cropping and direction classifier, guides SVTR for a comprehensive system, ensuring seamless detection and recognition of handwritten Chinese characters. Experimental results indicate that this system achieves a promising performance and can provide accurate information on handwritten Chinese characters.

In this paper, we present a DAV-based system for text (mainly English and Chinese) detection and recognition. With the combination of unmanned aerial vehicle and scene text recognition, the system can realize text detection and recognition in long-range air-plane images, providing an underlay for unmanned navigation and fast text information understanding. Robust text detection and accurate text recognition can be achieved by two contributions. First, a scalable engine is proposed to synthesize text images by overlaying English or Chinese text into existing images in a natural way. Second, an framework which is trainable and end-to-end by combining Convolutional Neural Network and Recurrent Neural Network is adapted to recognize the variable-length text with a high accuracy. Field experiments are performed with different videos shot in various backgrounds and outdoors to show that the proposed system can detect and recognise text information in UAV imagery robustly and effectively.

Scene text recognition is a well-rooted research domain covering a diverse application area. Recognition of scene text is challenging due to the complex nature of scene images. Various structural characteristics of the script also influence the recognition process. Text and background segmentation is a mandatory step in the scene text recognition process. A text recognition system produces the most accurate results if the structural and contextual information is preserved by the segmentation technique. Therefore, an attempt is made here to develop a robust foreground/background segmentation(separation) technique that produces the highest recognition results. A ground-truth dataset containing Devanagari scene text images is prepared for the experimentation. An encoder-decoder convolutional neural network model is used for text/background segmentation. The model is trained with Devanagari scene text images for pixel-wise classification of text and background. The segmented text is then recognized using an existing OCR engine (Tesseract). The word and character level recognition rates are computed and compared with other existing segmentation techniques to establish the effectiveness of the proposed technique.

Scene Text Recognition (STR) has become a popular and long-standing research problem in computer vision communities. Almost all the existing approaches mainly adopt the connectionist temporal classification (CTC) technique. However, these existing approaches are not much effective for irregular STR. In this research article, we introduced a new encoder-decoder framework to identify both regular and irregular natural scene text, which is developed based on the transformer framework. The proposed framework is divided into four main modules: Image Transformation, Visual Feature Extraction (VFE), Encoder and Decoder. Firstly, we employ a Thin Plate Spline (TPS) transformation in the image transformation module to normalize the original input image to reduce the burden of subsequent feature extraction. Secondly, in the VFE module, we use ResNet as the Convolutional Neural Network (CNN) backbone to retrieve text image features maps from the rectified word image. However, the VFE module generates one-dimensional feature maps that are not suitable for locating a multi-oriented text on two-dimensional word images. We proposed 2D Positional Encoding (2DPE) to preserve the sequential information. Thirdly, the feature aggregation and feature transformation are carried out simultaneously in the encoder module. We replace the original scaled dot-product attention model as in the standard transformer framework with an Optimal Adaptive Threshold-based Self-Attention (OATSA) model to filter noisy information effectively and focus on the most contributive text regions. Finally, we introduce a new architectural level bi-directional decoding approach in the decoder module to generate a more accurate character sequence. Eventually, We evaluate the effectiveness and robustness of the proposed framework in both horizontal and arbitrary text recognition through extensive experiments on seven public benchmarks including IIIT5K-Words, SVT, ICDAR 2003, ICDAR 2013, ICDAR 2015, SVT-P and CUTE80 datasets. We also demonstrate that our proposed framework outperforms most of the existing approaches by a substantial margin.

Scene text recognition is very challenging due to the complex background, low resolution, perspective distortion and curved placement, etc. Most of the state-of-the-art methods adopt the attention-based encoder-decoder framework, and usually get suboptimal recognition performance for challenging text images due to the misalignment between attention region and target character region. In this paper, a novel module, named Gated Cascade Attention Module (GCAM), is proposed to increase the alignment precision of attention in a cascade way. Moreover, a channel and spatial attention module is introduced into the encoder to extract more discriminative features for text recognition. By assembling these two modules, a novel scene text recognizer is developed, and extensive experiments demonstrate it can achieve state-of-the-art results on multiple benchmarks of regular and irregular text images.

Scene text image super-resolution (STISR) aims to improve the resolution and visual quality of low-resolution (LR) scene text images, while simultaneously boost the performance of text recognition. However, most of the existing STISR methods regard text images as natural scene images, ignoring the categorical information of text. In this paper, we make an inspiring attempt to embed text recognition prior into STISR model. Specifically, we adopt the predicted character recognition probability sequence as the text prior, which can be obtained conveniently from a text recognition model. The text prior provides categorical guidance to recover high-resolution (HR) text images. On the other hand, the reconstructed HR image can refine the text prior in return. Finally, we present a multi-stage text prior guided super-resolution (TPGSR) framework for STISR. Our experiments on the benchmark TextZoom dataset show that TPGSR can not only effectively improve the visual quality of scene text images, but also significantly improve the text recognition accuracy over existing STISR methods. Our model trained on TextZoom also demonstrates certain generalization capability to the LR images in other datasets.

Text recognition in natural scene images has always been a hot topic in the field of document-image related visual sensors. The previous literature mostly solved the problem of horizontal text recognition, but the text in the natural scene is usually inclined and irregular, and there are many unsolved problems. For this reason, we propose a scene text recognition algorithm based on a text position correction (TPC) module and an encoder-decoder network (EDN) module. Firstly, the slanted text is modified into horizontal text through the TPC module, and then the content of horizontal text is accurately identified through the EDN module. Experiments on the standard data set show that the algorithm can recognize many kinds of irregular text and get better results. Ablation studies show that the proposed two network modules can enhance the accuracy of irregular scene text recognition.

A hybrid deep-based model for scene text detection and recognition in meter reading