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Textual information in a video is very useful for video indexing and retrieving. Detecting text blocks in video frames is the first important procedure for extracting the textual information. Automatic text location is a very difficult problem due to the large variety of character styles and the complex backgrounds.

In this paper, we describe the various steps of the proposed text detection algorithm. First, the gray scale edges are detected and smoothed horizontally. Second, the edge image is binarized, and run length analysis is applied to find candidate text blocks. Finally, each detected block is verified by an improved logical level technique (ILLT). Experiments show this method is not sensitive to color/texture changes of the characters, and can be used to detect text lines in news videos effectively.

This paper proposes a new neuro-fuzzy technique suitable for binarization and gray-scale reduction of digital documents. The proposed approach uses both the image gray-scales and additional local spatial features. Both, gray-scales and local feature values feed a Kohonen Self-Organized Feature Map (SOFM) neural network classifier. After training, the neurons of the output competition layer of the SOFM define two bilevel classes. Using the content of these classes, fuzzy membership functions are obtained that are next used by the fuzzy C-means (FCM) algorithm in order to reduce the character-blurring problem. The method is suitable for improving blurring and badly illuminated documents and can be easily modified to accommodate any type of spatial characteristics.

We present a method to assign to either the foreground or the background the regions into which a gray-level image is partitioned by watershed transformation. Our method is inspired by visual perception in the sense that the border separating any foreground component from the background is detected in correspondence with the locally maximal gray-level changes through the image. The method is implemented as consisting of three steps. The first two steps perform a basic assignment of the regions, while the remaining step examines again some regions tentatively assigned to the background during the second step and possibly changes their status. A feature of the method is that a hierarchical ranking of the regions assigned to the foreground is also accomplished.

Most of the fingerprint matching techniques require extraction of minutiae that are ridge endings or bifurcations of ridge lines in a fingerprint image. Crucial to this step is either detecting ridges from the gray-level image or binarizing the image and then extracting the minutiae. In this work, we firstly exploit the property of almost equal width of ridges and valleys for binarization. Computing the width of arbitrary shapes is a nontrivial task. So, we estimate the width using Euclidean distance transform (EDT) and provide a near-linear time algorithm for binarization. Secondly, instead of using thinned binary images for minutiae extraction, we detect minutiae straightaway from the binarized fingerprint images using EDT. We also use EDT values to get rid of spurs and bridges in the fingerprint image. Unlike many other previous methods, our work depends minimally on arbitrary selection of parameters.

This paper presents a new technique for adaptive binarization of degraded document images. The proposed technique focuses on degraded documents with various background patterns and noise. It involves a preprocessing local background estimation stage, which detects for each pixel that is considered as background one, a proper grayscale value. Then, the estimated background is used to produce a new enhanced image having uniform background layers and increased local contrast. That is, the new image is a combination of background and foreground layers. Foreground and background layers are then separated by using a new transformation which exploits efficiently, both grayscale and spatial information. The final binary document is obtained by combining all foreground layers. The proposed binarization technique has been extensively tested on numerous documents and successfully compared with other well-known binarization techniques. Experimental results, which are based on statistical, visual and OCR criteria, verify the effectiveness of the technique.

Numerous research based on offline Tamil recognition deals only with few Tamil characters since it becomes extremely complicated in distinguishing small variations in large handwritten document. The writer’s complexity affects the overall formation of the characters. Such types of complexities are due to discontinuation of structures, unnecessary over loops, variation in shapes as well as irregular curves. This complex issue results in enhanced error value rate. Therefore, to conquer such issues, this paper proposes a novel approach to enhance the offline Tamil handwritten character recognition by utilizing four principal steps: pre-processing, segmentation, feature extraction and classification. For optimal segmentation of Tamil characters, this paper utilizes the Tsallis entropy approach-based atom search (TEAS) optimization algorithm. Then a Newton algorithm based deep convolution extreme learning (DELM) approach is utilized for the extraction and classification of input images. Finally, experiments are carried out for numerous Tamil handwritten recognition-based approaches. The proposed Tamil character recognition utilizes the datasets of isolated Tamil handwritten characters established by HP lab India to evaluate the efficiency of the system.

Digital recognitions are playing a vital function in the current era of technological advancements. Hence, they offer more possible ways of performing handwritten character recognition (HCR). Generally, recognizing the Tamil handwritten texts is highly complicated, in comparison to the Western scripts. Nevertheless, many researchers have presented several real-time approaches to achieve Tamil character recognition (TCR) in offline mode. This paper introduces a new handwritten TCR (HTCR) approach with two phases: (1) pre-processing and (2) classification. Primarily, the scanned document in the Tamil language is pre-processed via the steps like RGB to grayscale conversion, binarization with thresholding, image complementation, application of morphological operations and linearization. The pre-processed images are then classified using an optimized convolutional neural network (CNN) model. Further, the fully connected layer (FCL) and the weights are tuned optimally via a new sea lion with self-adaptiveness (SL-SA) algorithm. Lastly, the adopted model is evaluated using various measures to prove its supremacy over the existing schemes.

Cursive handwriting recognition (CHWR) is an interesting area of research as it has a wide range of applications but lacks an accurate approach to provide better results due to its character shapes, the non-uniform spacing between words and within a word, diverse placements of dots, and diacritics, and very low inter-class variation among individual classes. A novel CHWR model is proposed to enhance the recognition accuracy with high global stability. The proposed model introduces three major phases: pre-processing, feature extraction and classification. In the pre-processing stage, the noise removal and binarization are adapted with the intrusion of improved adaptive wiener filtering (IAWF) and structural symmetric pixels. A hybrid deep direction distribution feature extraction (HDDDFE) approach is proposed to extract directional Local gradient histogram (LGH), column gradient histogram (CGH) features and a wavelet convolutional neural network with Block Attention Module (WCNN-BAM) is proposed to extract deep global features (GF), profile features (PF) and dynamic features (DF). A novel double hidden layer gated recurrent neural network with a feature attention mechanism (ODHL-GRNN-FAM) is proposed to offer handwritten classification results. The developed model is evaluated with the IAM database and attains an overall recognition accuracy of 98%, precision of 97%, f-measure of 97.99%, character error rate (CER) of 1.23%, word error rate (WER) of 4.8%, respectively.

Check forms are used by many people in daily life for money remittances. Surprisingly, the processing of these forms at banks and post offices is only partly automated. In this paper, we consider a particular kind of form, viz., the GIRO check forms used in Switzerland. We will describe a fully automatic system which is able to recognise the financial institution, the name and address of the receiver, and the account number on a GIRO check. The system comprises procedures for binarization, segmentation, model matching, and optical character recognition (OCR). Experiments on a sample set of 48 checks have shown promising results in terms of both computation time and recognition accuracy.

Digital watermarking has been proposed for the protection of digital medias. This paper presents two watermarking algorithms for binary images. Both algorithms involve a blurring preprocessing and a biased binarization. After the blurring, the first algorithm embeds a watermark by modifying the DC components of the Discrete Cosine Transform (DCT), followed by a biased binarization, and the second one embeds a watermark by directly biasing the binarization threshold of the blurred image, controlled by a loop. Experimental results show the imperceptibility and robustness aspects of both algorithms.

Generation of random fields (GRF) for image segmentation represents partitioning an image into different regions that are homogeneous or have similar facets of the image. It is one of the most challenging tasks in image processing and a very important pre-processing step in the fields of computer vision, image analysis, medical image processing, pattern recognition, remote sensing, and geographical information system. Many researchers have presented numerous image segmentation approaches, but still, there are challenges like segmentation of low contrast images, removal of shadow in the images, reduction of high dimensional images, and computational complexity of segmentation techniques. In this review paper, the authors address these issues. The experiments are conducted and tested on the Berkely dataset (BSD500), Semantic dataset, and our own dataset, and the results are shown in the form of tables and graphs.

India is rich in its heritage and culture. It has many historical monuments and temples where the walls are made of inscribed stones and rocks. The stone inscriptions play a vital role in portraying about the ancient incidents. Hence, the digitization of these stone inscriptions is necessary and contributes much for the epigraphers. Recently, the digitizing of these inscriptions began with the binarization process of stone inscriptions. This process mainly depends on the thresholding technique. In this paper, the binarization of terrestrial and underwater stone inscription images is preceded by a contrast enhancement and succeeded by edge-based filtering that minimizes noise and fine points the edges. A new method called modified bi-level thresholding (MBET) algorithm is proposed and compared with various existing thresholding algorithms namely Otsu method, Niblack method, Sauvola method, Bernsen method and Fuzzy C means method. The obtained results are evaluated with the performance metrics such as peak signal-to-noise ratio (PSNR) and standard deviation (SD). It is observed that the proposed method has an improvement of 49% and 39%, respectively, on an average by the metrics considered.

A novel approach of edge detection is proposed that utilizes a bidimensional empirical mode decomposition (BEMD) method as the primary tool. For this purpose, a recently developed fast and adaptive BEMD (FABEMD) is used to decompose the given image into several bidimensional intrinsic mode functions (BIMFs). In FABEMD, order statistics filters (OSFs) are employed to get the upper and lower envelopes in the decomposition process, instead of surface interpolation, which enables fast decomposition and well-characterized BIMFs. Binarization and morphological operations are applied to the first BIMF obtained from FABEMD to achieve the desired edges. The proposed approach is compared with several other edge detection methodologies, which include a combination of classical BEMD and morphological processing, the Canny and Sobel edge detectors, as well as combinations of BEMD/FABEMD and Canny/Sobel edge detectors. Simulation results with real images demonstrate the efficacy and potential of the proposed edge detection algorithm employing FABEMD.

Check forms are used by many people in daily life for money remittances. Surprisingly, the processing of these forms at banks and post offices is only partly automated. In this paper, we consider a particular kind of form, viz., the GIRO check forms used in Switzerland. We will describe a fully automatic system which is able to recognise the financial institution, the name and address of the receiver, and the account number on a GIRO check. The system comprises procedures for binarization, segmentation, model matching, and optical character recognition (OCR). Experiments on a sample set of 48 checks have shown promising results in terms of both computation time and recognition accuracy.

Character segmentation for license plate is a key component of an automatic vehicle license recognition system. A novel adaptive approach of character segmentation for badly degraded images is proposed. First, the non-uniform illumination correction, contrast enhancement and binarization are introduced; then, op- bottom edges of characters can be obtained by the units of the plate horizontal projection respectively; finally, the candidate regions of characters are located according to the plate vertical projection for coarse segmentation. And the character segmentation regions are determined by prior knowledge of license plate. The experimental results based on Chinese license plates shows that the proposed method is fast and accurate, and is tolerant to license plate with deformations, rotations, plate frame, rivet, the space mark, and so on.