Multi-resolution Character Recognition by Adaptive Classification

evelopment of OCRs for Indian script is an active area of activity today. Optical character recognition (OCR) is the mechanical or electronic translation of images of handwritten, typewritten or printed text (usually captured by a scanner) into machine-editable text. In simple words OCR is a visual recognition process that turns printed or written text into an electronic character-based file. OCR is a field of research in pattern recognition, artificial intelligence and machine vision. Though academic research in the field continues, the focus on OCR has shifted to implementation of proven techniques. A lot of work had been carried out for OCR at international scenario but in Indian context a concrete approach for character recognition is still required as scripts of Indian languages are from the group of most complex scripts and it is very hard to recognize them. Indian scripts present great challenges to an OCR designer due to the large number of letters in the alphabet, the sophisticated ways in which they combine, and the complicated graphemes they result in. The problem is compounded by the unstructured manner in which popular fonts are designed. There is a lot of common structure in the different Indian scripts. All existing OCR systems developed for various Indian scripts do not provide sufficient efficiency due to various factors. The objective of this paper is to discuss a more efficient character recognition technique. This paper introduces a new technical approach to recognize Indian script characters which are unpredictable due to different problems in other OCR‟s.

Character recognition plays an important role in the modern world. It makes human's job easier when solving more complex problems. We have developed and implemented a rotation invariant character recognition technique by extending the eigenface method previously presented in the literature. Our proposed approach treats each character as a two-dimensional recognition problem, taking advantage of the fact that characters can be described by a small set of 2D characteristic views of different angles (0deg-360deg). Character images of different angles are projected onto a feature space (eigen space) that best encodes the variation among known character images. The proposed system is based on principal component analysis which makes it efficient in learning and recognizing characters of different angle.

Character recognition in low quality and low-resolution images is still a challenging problem. In this paper a gray-scale image based character recognition algorithm is proposed, which is specially suit to gray scale images captured from real world or very low quality character recognition. In our research, we classify the deformations of the low quality and low-resolution character images into two categories: (1) High spatial frequency deformations derived from either the blur distortion by the point spread function (PSF) of scanners or cameras, random noises, or character deformations; (b) Low spatial frequency deformations mainly derived from the large- scale background variations. The traditional recognition methods based on binary images cannot give satisfactory results in these images because these deformations will result in great amount of strokes touch or stroke broken in the binarization process. In the proposed method, we directly extract transform features on the gray-scale character images, which will avoid the shortcomings produced by binarization process. Our method differs from the existing gray-scale methods in that it avoids the difficult and unstable step of finding character structures in the images. By applying adequate feature selection algorithms, such as linear discriminant analysis (LDA) or principal component analysis (PCA), we can select the low frequency components that preserve the fundamental shape of characters and discard the high frequency deformation components. We also develop a gray- level histogram based algorithm using native integral ratio (NIR) technique to find a threshold to remove the backgrounds of character images while maintaining the details of the character strokes as much as possible. Experiments have shown that this method is especially effective for recognition of images of low quality and low-resolution.

Setting false targets is one of the important means of battlefield camouflage. The survivability of protected targets largely depends on the effect of photoelectric deception and jamming of false targets. The comprehensive use of robust image features plays a key role in correctly evaluating the photoelectric deception jamming efficiency of false targets and quickly identifying true and false targets. The existing efficiency evaluation models lack systematic research on the robustness of various image features in different environments. There are common problems of single target background and incomplete consideration of environmental factors, which lead to the instability of the extracted target image features, and then affect the evaluation results. Given the above problems, two kinds of environmental conditions are set: the change of observation distance and the change of atmospheric attenuation intensity. The experimental environment is simulated by an equivalent simulation method. After filtering the target background interference and extracting the target subject image and features, the similarity measurement method is used to calculate the similarity between true and false target subject images. By comparing the changes of gray distribution, contour, and texture feature similarity value, the robustness ranking and change reasons of features under different environmental conditions are summarized. At the same time, experiments are designed to verify the robustness of gray distribution features. The results show that the gray distribution feature has strong robustness, and the combination of gray distribution feature on the battlefield can effectively help officers and soldiers identify true and false targets in a complex environment.

Ahstract-This study provides a novel brain-computer interface (BCI) approach for character recognition. The character recognition task is a two class classification problem. The key objective of the character recognition is to detect the P300 signal from the set of electroencephalogram (EEG) signals. The character is predicted from the detected P300 signal and row/column information of the oddball paradigm. The signal-to-noise ratio (SNR) of the electroencephalogram (EEG) signal is low. Ensemble of classifier is used to reduce the classifier variabilIty and enhance the SNR of the acquired signal. Here ensemble of SVMs (ESVMs) is used as classifier. The distribution of dataset is imbalanced due to its paradigm. A novel approach is applied in this work to balance the dataset. The proposed algorithm is evaluated on dataset IIb of BCI Competition II and dataset II of the BCI Competition III. The experimental results show that the proposed method outperforms the state-of-the-art character recognition performance.

Background: The growing use of smart hand-held devices in the daily lives of the people urges for the requirement of online handwritten text recognition. Online handwritten text recognition refers to the identification of the handwritten text at the very moment it is written on a digitizing tablet using some pen-like stylus. Several techniques are available for online handwritten text recognition in English, Arabic, Latin, Chinese, Japanese, and Korean scripts. However, limited research is available for Indic scripts. Objective: This article presents a novel approach for online handwritten numeral and character (simple and compound) recognition of three popular Indic scripts - Devanagari, Bengali and Tamil. Methods: The proposed work employs the Zone wise Slopes of Dominant Points (ZSDP) method for feature extraction from the individual characters. Support Vector Machine (SVM) and Hidden Markov Model (HMM) classifiers are used for recognition process. Recognition efficiency is improved by combining the probabilistic outcomes of the SVM and HMM classifiers using Dempster-Shafer theory. The system is trained using separate as well as combined dataset of numerals, simple and compound characters. Results: The performance of the present system is evaluated using large self-generated datasets as well as public datasets. Results obtained from the present work demonstrate that the proposed system outperforms the existing works in this regard. Conclusion: This work will be helpful to carry out researches on online recognition of handwritten character in other Indic scripts as well as recognition of isolated words in various Indic scripts including the scripts used in the present work.

<p>Text recognition task involves recognizing the text from the natural image; it possesses various application, which aids information extraction through data mining from street view like images. Scene text recognition involves two stages i.e., text detection and text recognition, in the past several mechanisms has been proposed for accurate identification, these mechanisms are either traditional approach or deep learning-based. All the existing deep-learning methodology fails as this comprises character data and image data, further this research develops an optimal architecture fusion neural network (FNN) for text identification and recognition. FNN comprises several layers of convolutional neural network as well as recurrent neural network. Within FNN architecture convolutional layer is utilized for the feature extraction and recurrent layer is utilized for attaining the feature classification prediction. Further, an optimal training architecture is established for the enhancement of classification accuracy. Here Devanagari MLT-19 dataset is utilized for the evaluation of FNN. Three different parameters are considered during evaluation i.e., script word identification, character recognition rate (CRR) and word recognition rate (WRR). Further comparison with existing models is performed to establish the proposed model efficiency and it shows FNN methodology observes 98.67% of script identification accuracy, 84.65% of WRR and 92.93% of CRR.</p>

A new approach to optical character recognition (OCR) is presented. Whereas existing OCR systems have been designed 20 obtain sufficient recognition accuracy, even though this does not provide any direct information useful for improving the systems, this approach uses genetic algorithms to analyze the feature space of the system by visualizing the forms of character images that correspond to the feature vectors in a way that humans can comprehend. It is shown that character images can be reconstructed from feature vectors by 100-generation iteration using genetic algorithms. Experimental results for visualizing reference vectors and category boundaries are presented. The results suggest the existence of ambiguous regions in category boundaries.

There is a special induction field in the figures of characters which is a physiological and psychological phenomenon and is highly related to human character recognition. In this paper, a formal definition of the field transform is introduced, in which the field of a character image is simulated by a 2-dimensional scalar field which is caused by all the stroke points in the character image. If can be applied to both binary and gray-level images. A very simple character recognition approach is also proposed based on the field transform and is applied to personal handwritten Chinese character recognition. Preliminary experimental results are very promising. Further efforts should be made to find out new ways of utilizing the field transform for character recognition and other applications.

This paper provides a classification methodology of Malayalam characters segmented from scanned document images. Optical Character Recognition (OCR) is one of the successful area which has wide variety of applications related to pattern recognition. This paper describes segmented character recognition using Singular Value Decom-position (SVD). Euclidean distance measure is used for finding the nearest character class of the segmented character image during testing. For each character class, a resultant template is created from training character images using the proposed approach, which in turn reduces the comparisons required for classification. The result obtained from the experiment shows that this method provides an accuracy of 97%.

Segmentation is a very critical stage in the character recognition process as the performance of any character recognition system depends heavily on the accuracy of segmentation. Although segmentation is a well-researched area, segmentation of handwritten text is still difficult owing to several factors like skewed and overlapping lines, the presence of touching, broken and degraded characters, and variations in writing styles. Therefore, researchers in this area are working continuously to develop new techniques for the efficient segmentation and recognition of characters. In the character recognition process, segmentation can be implemented at the line, word, and character level. Text line segmentation is the first step in the text/character recognition process. The line segmentation methods used in the character recognition of handwritten documents are presented in this paper. The various levels of segmentation which include line, word, and character segmentation are discussed with a focus on line segmentation.KeywordsSegmentationOCRPattern recognitionLine segmentationWord segmentationCharacter segmentationTouching lines segmentationOverlapping lines segmentation

AGV (Automated Guided Vehicle) equipped with artificial intelligence (AI) is anticipated to boost Indonesia's industrial development. The little computer used to generate this robot's artificial intelligence used mechanical gears similar to those found in an eight-wheeled, two-wheel-drive car (2WD). This article outlines and demonstrates the usage of an AGV-controlled approach to determine a place inside a building by detecting text in different locations throughout the building. The current technique employs the programming languages Python and OpenCV. Optical Character Recognition (OCR) has been tweaked or enhanced for usage with OpenCV. Multiple texts are read using OCR as the principal technique. In this instance, OCR functions at many stages of the process, in addition to being employed for exploring letters and words, word translation, character classification, linguistic analysis, and adaptive character classification. The output text from the system's document processing procedure will likely contain the location or even the position of an AGV robot once the process has concluded. This text is produced from the text that was previously submitted using the camera function. After a thorough search, the AGV robot will go to the next area before returning to its starting point. The method above can be implemented on the AGV lab's hardware, which has a solid basis

In this article the GCDB (Greek Characters DataBase) System is presented. GCDB is a complete database system for storing images of Greek unconstrained handwritten characters. The three elements that compose this database are: a specialized input form, a database that contains the images of the filled forms and the software that allows the inputting of the data from the form into the database and their retrieval. The main purpose of this database system is to make it possible to achieve the automatic storage and organization of images of Greek symbols and letters in view of their use by OCR (Optical Character Recognition) systems or other applications. The GCDB system is designed within the concept of future expansion, providing an up to date database of Greek handwritten characters to cover the growing demands for offline character recognition of the Greek language.

With the evolving technological era, the optical character recognition systems have substantial execution, considering the widespread use of daily hand-written human transaction. Optical Character Recognition (OCR) is an implementation of Computer Vision that digitizes numerous hand dealt documents for further analysis and formatting. OCR is achieved by various ways such as discriminative analysis and deep learning. This paper focuses on evaluating deep learning models on a hand-written compiled dataset of Urdu digits. The evaluation is performed for deep convolutional learning algorithms; VGGNet16, InceptionV3, ResNet50, and DenseNet121. The convolutional models are pre-trained on the ImageNet. The model weights of fully connected layers have been evaluated, reducing the training time of the convolutional layers. The testing accuracy for the compiled dataset is observed as, ResNet50 with 96%, InceptionV3 with 95%, VGGNet16 with 95% and DenseNet121 with 94%.

To improve the readability, image compression, and optical character recognition (OCR) system performance for two-tone (binary) text image data, we investigated morphological methods of image processing. We found them to be fast and effective not only with noise text images but with relatively noise-free images as well. Using morphology, we improved text image readability as judged in a blind test, increased compression ratio using CCITT Group 4, and reduced OCR error (cluster) rates in a commercial omnifront scanner.