A COMPREHENSIVE STUDY ON TEXT INFORMATION EXTRACTION FROM NATURAL SCENE IMAGES

Now days reading words from an unconstrained and noisy image is not easy. Text localization and recognition in an image is a research area which takes efforts to develop a computer system with an ability to automatically read the text from images. The Optical Character Recognition (OCR) tool gives good results obtained to read the text from an image. The objective of this study is to propose a new method for text localization and recognition in natural scene images with complex background. In this paper, a hybrid methodology is suggested which extracts text from natural scene image with chaotic backgrounds. The proposed approach involves four stages. First, superimposed text regions in an image are extracted based on character descriptors features like Area, Bounding box, Perimeter, Euler number, Horizontal crossings. In the second step, superimposed text regions are tested for text content or nontext using character descriptors and SVM classifier. In the third step, detection of multiple lines in localized text regions is done and line segmentation is performed using horizontal profiles. In the final step, using vertical profiles each character of the segmented line is extracted. The workout has been done using images drawn from ICDAR 2013 and SVT 2010 datasets. The results demonstrate the effectiveness of the proposed method, which can be used as an efficient method for text localization and recognition in natural scene images.

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> .

Extracting text character from natural scene images is a complex task due to differences in text style, font, size, orientation, alignment and background variation. Texts in images conveys important information, which helps to make images more understandable. A new hybrid method for text detection from natural scene images, proposes an enhanced model based on fractional poisson to increases the quality of the images obtained by Laplacian operation. Here image enhancement operation refers the actual values of pixels to obtain better contrast between targets and their backgrounds. After the enhancement, images looks brighter than input and Laplacian images. The Enhanced image does not contain any noise as well as in Laplacian image. It occurs text detection and recognition methods to achieve better accuracies. The Maximally Stable Extremal Region algorithm has been used to detect almost all characters or text in the image, even if in low quality. It is a connected component of some gray level sets of the image. A new method adopts for word segmentation that uses the bounding box regions to enclose the letters or characters of the words, then the resulting letter spaces are continuously filled to combine the character bounding boxes and to get the word bounding boxes.

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.

The natural scene images contain valuable information about themselves in the form of textual matter present in them. The process of extracting text regions can be used to understand the context of the image, that can be of great help in many applications helpful for humanity. The extraction of text regions from natural scene images, which is a daunting task due to variation in text elements in the form of size, orientation, colors, low contrast images and complicated background. In this paper, we propose a method based on Maximal Stable Extremal Region (MSER) and weighted median filter along with three text specific traits to identify and extract text regions by creating bounding box around them in natural scene images. The image is passed through a weighted median filter to preserve and smoothen the edges followed by candidate region extraction by MSER. Heuristics rules filter the non-text components. Finally, the classification process is carried out with the help of classifiers (using adaboost.m1 and k-nn) to classify candidate text regions and non-text regions based on three text specific traits, followed by the grouping of text components in text line using clustering. The method aims to extract text regions robustly from low contrast images. The performance of the method is checked on ICDAR 2011 testing dataset to prove its efficiency concerning precision, recall, and f-measure.

This paper implements a new text region recognition algorithm that can accurately localize image text regions in natural image with complex background. The method is mainly based on the anchor mechanism of the faster R-CNN, taking into account the special features of the text area relative to other object detect tasks, so as to convert the text area detection task in the image into a general object detection task for the small area text. In this way, we can detect the text proposal directly in the convolutional feature map of the neural network, and it can simultaneously predict the text/non- text score of the proposal and the coordinates of each proposal in the image. Then we propose a text line construction algorithm that can combine the text regions into complete text line blocks, thus greatly improving the accuracy and reliability of our text detection model. Our text detector also works accurately in multi-scale and multi-lingual text detection tasks. It achieves 0.86 F-measure and 0.78 F-measure on the ICDAR 2011 and ICDAR 2013 benchmarks, which also confirms the accuracy of our model.

Text extraction from scene images by character appearance and structure modeling

Textual matter present in a natural scene image provides indispensable information about it. The semantics and information present in the natural scene images can be perceived by extracting the text regions in them. Detection and localization of text from natural scene images is a challenging task for analysis of images due to various font size, font type, and illumination. In this paper, we propose a hybrid approach for text detection and localization based on text confidence score using three attributes namely stroke width dissimilarity, color dissimilarity and occupy rate convex area to discern text and non-text constituents. The aim of this paper is to achieve fast detection and localization of text regions in low resolution and blurred images. To accomplish this, the possible candidate regions are extracted using edge smoothing by fast guided filter followed by MSER. The text confidence score on these constituents is calculated using the Bayesian framework with the help of above mentioned three attributes. Experimental results on benchmark ICDAR 2013 testing dataset shows the efficacy of our method in the form of precision, recall, and f-measure.

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.

In this paper, we proposed feature extraction method in natural scene images. And also, we develop character recognition system using feature extraction method in natural scene images. A graph matching method use structural feature of character. It is recognition method that it considers relation of position and structural relation. It is robust method to change of font or rotation. In order to proof it, we tested two cases that training font and test font are same case or difference case.

Text data in an image present useful information for annotation, indexing and structuring of images. The gathered information from images can be applied for devices for impaired people, navigation, tourist assistance or georeferencing business. In this paper we propose a novel algorithm for text detection and localization from outdoor/indoor images which is robust against different font size, style, uneven illumination, shadows, highlights, over exposed regions, low contrasted images, specular reflections and many distortions which makes text localization task harder. A binarization algorithm based on difference of gamma correction and morphological reconstruction is realized to extract the connected components of an image. These connected components are classified as text and non test using a Random Forest classifier. After that text regions are localized by a novel merging algorithm for further processing.

Cursive text detection and recognition in natural scene images are complex tasks due to the variability and intricacy of handwriting styles. This research focuses on developing a deep learning-based approach to address these challenges. The proposed solution leverages advancements in deep learning techniques to improve the accuracy and robustness of cursive text detection and recognition. The research involves collecting and annotating a diverse dataset of natural scene images containing cursive text. A deep learning model for cursive text detection is trained on the annotated dataset, and techniques for text line segmentation are investigated to enhance accuracy. Furthermore, a deep learning-based recognition system is designed and implemented to transcribe cursive text into machine-readable text. The proposed approach is evaluated and compared with existing methods using appropriate metrics and benchmark datasets. The research aims to provide insights into the challenges and opportunities of cursive text analysis in real-world scenarios and contribute to advancements in document digitization, handwriting analysis, and information retrieval.

Automatic Number Plate Recognition system is an application of computer vision and image processing technology that takes photograph of vehicles as input image and by extracting their number plate from whole vehicle image , it display the number plate information into text. Mainly the ANPR system consists of 4 phases: Acquisition of Vehicle Image and PreProcessing, Extraction of Number Plate Area, Character Segmentation and Character Recognition. The overall accuracy and efficiency of whole ANPR system depends on number plate extraction phase as character segmentation and character recognition phases are also depend on the output of this phase. Further the accuracy of Number Plate Extraction phase depends on the quality of captured vehicle image. Higher be the quality of captured input vehicle image more will be the chances of proper extraction of vehicle number plate area. The existing methods of ANPR works well for dark and bright/light categories image but it does not work well for Low Contrast, Blurred and Noisy images and the detection of exact number plate area by using the existing ANPR approach is not successful even after applying existing filtering and enhancement technique for these types of images. Due to wrong extraction of number plate area, the character segmentation and character recognition are also not successful in this case by using the existing method. To overcome these drawbacks I proposed an efficient approach for ANPR in which the input vehicle image is pre-processed firstly by iterative bilateral filtering , adaptive histogram equalization and number plate is extracted from pre-processed vehicle image using morphological operations, image subtraction, image binarization/thresholding, sobel vertical edge detection and by boundary box analysis. Sometimes the extracted plate area also contains noise, bolts, frames etc. So the extracted plate area is enhanced by using morphological operations to improve the quality of extracted plate so that the segmentation phase gives more successful output. The character segmentation is done by connected component analysis and boundary box analysis and finally in the last character recognition phase, the characters are recognized by matching with the template database using correlation and output results are displayed. This approach works well for low contrast, blurred, noisy as well as for dark and light/bright category images. The comparison is done between the ANPR with Adaptive Histogram Equalization and Iterative Bilateral Filtering that is the proposed approach and the existing ANPR approach using metrics: MSE, PSNR and Success rate.

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.

In this paper, a new method of detecting text regions from video sequences, without prior knowledge such as font, color, size of characters is proposed. By analyzing the characteristics of text regions in video sequences, we found that text regions include both horizontal and vertical edges simultaneously, while background regions do not. Therefore, In order to detect text regions accurately, the detection of candidate object regions through 1D-DCT and an adaptive thresholding technique are proposed. Experimental results have shown that the proposed method successfully detects text regions regardless of environmental conditions such as low contrast, complex texture, and various colors and sizes.