Scene Text Detection Using Superpixel-Based Stroke Feature Transform and Deep Learning Based Region Classification

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.

Text recognition in natural scene images is a challenging problem in computer vision. Different than the optical character recognition (OCR), text recognition in natural scene images is more complex due to variations in text size, colors, fonts, orientations, complex backgrounds, occlusion, illuminations and uneven lighting conditions. In this paper, we propose a segmentation-free method based on a deep convolutional recurrent neural network to solve the problem of cursive text recognition, particularly focusing on Urdu text in natural scenes. Compared to the non-cursive scripts, Urdu text recognition is more complex due to variations in the writing styles, several shapes of the same character, connected text, ligature overlapping, stretched, diagonal and condensed text. The proposed model gets a whole word image as an input without pre-segmenting into individual characters, and then transforms into the sequence of the relevant features. Our model is based on three components: a deep convolutional neural network (CNN) with shortcut connections to extract and encode the features, a recurrent neural network (RNN) to decode the convolutional features, and a connectionist temporal classification (CTC) to map the predicted sequences into the target labels. To increase the text recognition accuracy further, we explore deeper CNN architectures like VGG-16, VGG-19, ResNet-18 and ResNet-34 to extract more appropriate Urdu text features, and compare the recognition results. To conduct the experiments, a new large-scale benchmark dataset of cropped Urdu word images in natural scenes is developed. The experimental results show that the proposed deep CRNN network with shortcut connections outperform than other network architectures. The dataset is publicly available and can be downloaded from <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://data.mendeley.com/datasets/k5fz57zd9z/1</uri> .

Text in scene images usually contains significant information. Text detection and recognition in the scene is important for a variety of advanced machine vision applications, such as image and video retrieval, automotive assistance, and multilingual translation. In particular, most text recognition systems require texts to be localized in images beforehand and this is a significant demand. The purpose of this study is to provide a method to detect texts in natural images. The proposed approach combines advantages of extremal region, ER, methods and classification of convolutional neural network, CNN. This significantly reduces the false positives and increases the accuracy of detection. The method of sliding windows is employed with different sizes in order to determine text candidates. Extraction of enhanced ERs is performed in three consecutive stages on three distinct color channels, R, G, and B. Then, the results are combined together by an add method. After grouping, the word candidates are classified to two classes of text and non-text sections by a CNN classifier. By applying non-maximum suppression (NMS) algorithm to the same words, words with the highest probability are selected. The average values of accuracy, recall, precision and F-measure of the proposed text detection model on the ICDAR2013 database are 0.893, 0.962, 0.948, and 0.955, respectively. The optimal cut point of the proposed method is 0.648, which has the highest average accuracy, 91.93%. The AUC of ROC and PR diagrams for the proposed model are 0.851 and 0.718, respectively. These results of AUC for ROC and PR curves showed an outstanding enhancement in comparison with the best detection rate of previous methods. Experimental results on the ICDAR2011, ICDAR2013 and ICDAR2015 databases also demonstrate that our algorithm outperforms the state-of-the-art scene text detection methods.

Scene text detection has attracted great interest from the computer vision and pattern recognition communities since text information plays an important role in image indexing and scene understanding. Deep neural networks have become popular for the task of scene text detection, especially for their ability to learn strong text features. However, existing deep learning based state-of-the-art scene text detection methods detect texts only from a single feature map which is unable to capture semantic information at all scales. In this paper, we propose a novel deep learning based model that leverages the pyramid structure of feature maps for accurate scene text detection. We also design a deep convolutional neural network model for non-maximum suppression. In addition, we develop a novel loss function and training method for end-to-end training. The experimental results validate that our end-to-end system is simple, fast, and achieves high accuracy on standard datasets, namely, ICDAR 2015 and MSRA-TD500. We also create a dataset for scene text detection.

As road traffic sign recognition is a crucial component for automatic driver assistance systems, it is a key problem in computer vision as well. Therefore, in this paper, we study on the problem of road traffic sign recognition utilising the computer vision technology. The main innovation of this paper is to propose an improved convolutional neural network, and then use it to tackle the road traffic sign recognition problem. Convolutional neural network can learn features from training data set, and a convolutional network contains alternating layers of convolution and pooling. Particularly, RGB traffic images are transformed to grey scale images, and then grey scale images are input to the improved convolutional neural network. Furthermore, the fixed layers are utilised to discover region of interests, and the learnable layers are used to extract features. In general, output information of the proposed two learnable layers are input to the classifier separately, and parameters of learnable layers and the classifier are trained at the same time. Finally, GTSDB data set is chosen to make performance evaluation, among which 600 images and 300 images are regarded as training and testing data set respectively. Experimental results demonstrate that the improved CNN-based traffic sign recognition performs better than the traditional CNN.

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.

We live in a visual world where text cues are abundant in urban environments. The premise for our work is for robots to capitalize on these text features for visual place recognition. A new technique is introduced that uses an end-to-end scene text detection and recognition technique to improve robot localization and mapping through Visual Place Recognition (VPR). This technique addresses several challenges such as arbitrary shaped text, illumination variation, and occlusion. The proposed model captures text strings and associated bounding boxes specifically designed for VPR tasks. The primary contribution of this work is the utilization of an end-to-end scene text spotting framework that can effectively capture irregular and occluded text in diverse environments. We conduct experimental evaluations on the Self-Collected TextPlace (SCTP) benchmark dataset, and our approach outperforms state-of-the-art methods in terms of precision and recall, which validates the effectiveness and potential of our proposed approach for VPR.

Text detection in natural scene images is a challenging problem in computer vision. To robust detect various texts in complex scenes, a hierarchical recursive text detection method is proposed in this paper. Usually, texts in natural scenes are not alone and arranged into lines for easy reading. To find all possible text lines in an image, candidate text lines are obtained using text edge box and conventional neural network at first. Then, to accurately find out the true text lines in the image, these candidate text lines are analyzed in a hierarchical recursive architecture. For each of them, connected components segmentation and hierarchical random field based analysis are recursively employed until the detected text line no more changes. Now the detected text lines are output as the text detection result. Experiments on ICDAR 2003 dataset, ICDAR 2013 dataset and Street View Dataset show that the hierarchical recursive architecture can improve text detection performance and the proposed method achieves the state-of-art in scene text detection.

Scene text in video contains semantic information and thus can contribute significantly to video retrieval and understanding. However, most methods detect scene text in image or single frame. Videos differ from images in temporal redundancy. In this paper we present a novel approach to detecting video scene text based on the video temporal redundancy. Video scene texts in consecutive frames have arbitrary motion due to camera or object movement. Therefore, first, we perform the motion detection in 30 consecutive frames to synthesize motion image. Second we implement video scene text detection in single frame to retrieve candidate text regions. Finally, the synthesized motion image is used to filter out candidate text regions and only keep the candidate text regions which have motion occurrence as final scene text. Experimental results show that our algorithm performs well for detecting scene text with various color, font-size and text alignment.

The main contribution of this paper is a novel Extended Kalman Filter (EKF) based localisation scheme that fuses two complementary approaches to outdoor vision based localisation. This EKF is aided by a front end consisting of two Convolutional Neural Networks (CNNs) that provide the necessary perceptual information from camera images. The first approach involves a CNN based extraction of information corresponding to artefacts such as curbs, lane markings, and manhole covers to localise on a vector distance transform representation of a binary image of these ground surface boundaries. The second approach involves a CNN based detection of common environmental landmarks such as tree trunks and light poles, which are represented as point features on a sparse map. Utilising CNNs to obtain higher level information about the environment enables this framework to avoid the typical pitfalls of common vision based approaches that use low level hand crafted features for localisation. The EKF framework makes it possible to deal with false positives and missed detections that are inevitable in a practical CNN, to produce a location estimate together with its associated uncertainty. Experiments using a Personal Mobility Device (PMD) driven in typical suburban streets are presented to demonstrate the effectiveness of the proposed localiser.

Visually impaired people suffer daily from their disability to read textual information. One of the most anticipated blind-assistive devices is a system equipped with a wearable camera capable of finding the textual information in natural scenes and translating it into sound through a speech synthesizer. To avoid duplicate readings, the device should be able to recognize text areas with the same content, and group them to obtain a single result. Scene text detection and tracking methods attract a lot of interest for these purposes. However, this field is still challenging and methods of scene text detection and tracking are yet to be perfected. This paper proposes a scene text tracking system capable of finding text regions and tracking them in video frames captured by a wearable camera. By combining a text detection method with a feature point tracker, we obtain a robust text tracker which produces much less false positive text images at 2.9 times faster speed compared with the conventional method.

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.

Image-based localization, or camera relocalization, is a fundamental problem in computer vision and robotics, and it refers to estimating camera pose from an image. Recent state-of-the-art approaches use learning based methods, such as Random Forests (RFs) and Convolutional Neural Networks (CNNs), to regress for each pixel in the image its corresponding position in the scene's world coordinate frame, and solve the final pose via a RANSAC-based optimization scheme using the predicted correspondences. In this paper, instead of in a patch-based manner, we propose to perform the scene coordinate regression in a full-frame manner to make the computation efficient at test time and, more importantly, to add more global context to the regression process to improve the robustness. To do so, we adopt a fully convolutional encoder-decoder neural network architecture which accepts a whole image as input and produces scene coordinate predictions for all pixels in the image. However, using more global context is prone to overfitting. To alleviate this issue, we propose to use data augmentation to generate more data for training. In addition to the data augmentation in 2D image space, we also augment the data in 3D space. We evaluate our approach on the publicly available 7-Scenes dataset, and experiments show that it has better scene coordinate predictions and achieves state-of-the-art results in localization with improved robustness on the hardest frames (e.g., frames with repeated structures).

Could scene context be beneficial for scene text detection?

Convolutional Neural Networks (CNNs) have recently been utilized in a wide range of applications, including image and pattern identification, voice recognition, biometric embedded vision, food recognition, and video analysis for surveillance, industrial robots, and autonomous vehicles. Convolutional neural networks (CNNs) are gaining popularity for a variety of reasons. Feature extractors are created by hand in classic image recognition models. The weights of the convolutional layer utilized for feature extraction, as well as the fully connected layer, are used for classification in CNNs, and these weights are set throughout the training phase. The goal of this study is to evaluate a few convolutional neural network (CNN) learning machine approaches for image recognition. Furthermore, machine learning algorithms are used extensively in contemporary approaches to picture recognition. This article focuses on the development trend of convolution neural network (CNNs) model owing to various learning methods in image recognition during the 2000s, which is largely introduced from the angles of capturing, verification, and clustering, based on twenty five journals that have been reviewed. As a result, deep convolutional neural networks (DCNNs) have been very effective in a variety of machine learning and computer vision problems because they provide a large quality gain at a low computational cost. This training approach also enables models with many processing layers to learn different degrees of abstraction for data representation.