Segmentation Of Script Research Articles

Pen Stroke Digit Recognition Using CNN

Hand-written character and digit recognition have been one of the most exigent and engrossing field of pattern recognition and image processing. The main aim of this paper is to demonstrate and represent the work which is related to hand-written digit recognition. The hand-written digit recognition is a very exigent task. In this recognition task, the numbers are not accurately written or scripted as they differ in shape or size; due to which the feature extraction and segmentation of hand-written numerical script is arduous. The vertical and horizontal projections methods are used for the purpose of segmentation in the proposed work. KNN is applied for recognition and classification. The digit recognition is a very exigent task. In this recognition task, the numbers are not accurately written or scripted as they differ in shape or size; due to which the feature extraction and segmentation of numerical script is arduous. The vertical and horizontal projections methods are used for the purpose of segmentation in the proposed work. CNN is applied for recognition, classification and we are also getting nice accuracy while doing prediction.

Last Teen Pixels for Arabic Font Size and Style Recognition

Arabic printed script segmentation and recognition techniques change from font to other i.e. each font has particular properties calligraphic and structural which differ with other. Majority of segmentation system suffer in word or sub word segmentation into characters because they consider one algorithm to segment all kind of Arabic printed font, style and size. The goal of this work is to prepare a system of word or sub word Optical Font Arabic Recognition (OFAR) for different font size and style of Arabic printed script, in order to integrate it in global Arabic Optical Character Recognition (AOCR) to choose preferred and good segmentation algorithm. APTI database was used to extract last ten pixels for each word or sub word to build new database of last 10 pixels for each word; OFAR is based upon this new database and our extraction approach called Pixels Continuity (PC) algorithm in different matrix direction and some histogram statistics to extract 20 features. Three KNN classifiers with K=5 and three different distances using Cityblock, Euclidean and Correlation based upon majority-vote are used to evaluate the system robustness. This classifier is compared in the first time with Back propagation Neural Network and Steerable Pyramid (SP) algorithm to re cognize three font families, then in the second time with Gaussian Mixture Models (GMMs) to recognize font and size. The average recognition results obtained was 99.55% about font and size and 98.17% for font, size and style recognition.

Komparasi Algoritma Denoising Dan Binarization Dengan Adaptive Thresholding Dan Morfologi Untuk Menigkatkan Kualitas Keterbacaan Citra Naskah Lontar (Takepan) Sasak

One of the cultural heritages in West Nusa Tenggara, especially Lombok, is takepan (ancient lontar manuscripts). Takepan written on Lontar leaf sheets (Duntan leaf in Sasak language) is known as the jejawan script (derived from ancient Javanese language). According to [1] in his research on image segmentation of Javanese script, the adaptive thresholding value was obtained based on the variation in the sensitivity of each local windows. The process of determining the thresholding value works on blocks in the image. Images that have high contrast and high lighting variations are very difficult to classify as background or foreground because there are a lot of pixels, you should use Local Adaptive Thresholding. From the research that has been done, it can be concluded that adaptive thresholding and morphology with dilation approach really help maximize image readability with the highest Peak Signal to Noise Ratio (PSNR) value reaching 34,107 dB DOI : 10.29408/jit.v3i2.2304

International, National and Local Languages OCR Segmentation of Running Hand Scripts

Script Segmentation of running hand scripts is a complicated job because of different hand writing styles and complex formational quality. Segmentation of moving hand script in Indian language is a difficult assignment .The incident of a title, crossway nature in the mid section & half nature makes the segmentation process is more difficult. Sometimes, the import space and noises make line shatter a hard task. Without disconnecting the nudge characters, it will be complicated to recognize the character; hence shatter is needed for the moving texts in a word. So, the technique, according to that first step will be pre-processing of a term, then we can identify the joint points, form the bounding boxes around all perpendicular & parallel lines of the script , finally splint the nudge nature based on their height and width. For non-touching printed scripts and running hand scripts, it gives us 95% and 90%result respectively. This technique fails for some hand written characters because of obligation that means cause will not match always to a lot of variation in the different handwriting scripts i.e., character like occurs problem as space in the middle of parts in scripts that can be resolve by identification system because of this system considers initial part as non-bar character and cut after that, as the future exertion is a concern, in this go these different characters as input to the character identification system. In this method have resolute control of a word which will once more helpful for the identification system to perceive bottom modifiers and higher modifiers. This running hand scripts technique can be applicable to other Indic languages hand written scripts.

Segmentation techniques for recognition of Arabic-like scripts: A comprehensive survey

Arabic script based text recognition system has been a popular field of research for many years that can be used in the learning and teaching process to the students and educators how to read and understand educational contents of Arabic script. The challenging nature of Arabic script recognition has attracted the attention of researchers from both industry and academic circles but these efforts have not achieved good results until now. Segmentation of Urdu script when written in Nasta’liq writing style is very difficult task due to the complexity of writing style as compare to Naskh writing style. Good segmentation is one of the reasons for high accuracy. Character segmentation has been a critical phase of the OCR process. The higher recognition rates for isolated characters as compare to results of words or connected character well illustrate the importance of segmentation. Current study investigates the recent work for character segmentation and challenges for segmentation for Arabic script based languages.

Line-wise Script Segmentation for Indian Language Documents

In a multi-lingual country like India, script segmentation or script separation of the multi-script in an image of a document page is of primary importance for a script identification system. For script segmentation of such a document page, it is necessary to segment multi script forms before running individual OCR of the script. In this paper we present a technique for script segmentation of the individual text line for printed in Indian language document. Our line wise script segmentation approach is Horizontal Projection Profile based script segmentation. A prototype of the system has been tested on printed Indian language lines of script and an average accuracy of 99% has been achieved.

Segmentation and Recognition of Arabic Printed Script

In this work we present a method for the recognition of Arabic printed script. The major problem of the automatic reading of cursive writing is a segmentation of script to isolate characters. The recognition process consists of four phases: Preprocessing, segmentation, feature extraction and the recognition.In the preprocessing, the image is scanned and smoothed. The correction of skew lines is done by using Hough transform . In the second phase, the text is segmented into lines, words or parts of words and each word into characters based on the principle of projection of the histogram. Features such as: density, profile, Hu moments and histogram are used to classifier the characters based on the Neural network. DOI: http://dx.doi.org/10.11591/ij-ai.v2i1.1236

A Feature-based Approach to Segmentation of Online Persian Cursive Script

A Feature-based Approach to Segmentation of Online Persian Cursive Script

Performance analysis of character segmentation approach for cursive script recognition on benchmark database

This paper analyzes the improved performance of our proposed character segmentation algorithm in comparison to others presented in the literature from accuracy and computational complexity points of view. The training set is taken from IAM and test set is from CEDAR benchmark databases. Segmentation is achieved by analyzing [email protected]?s geometric features and ligatures which are strong points for segmentation in cursive handwritten words. Following pre-processing, a new heuristic technique is employed to over-segment each word at potential segmentation points. Subsequently, a simple criterion is adopted to come out with fine segmentation points based on character shape analysis. Finally, the fine segmentation points are fed to train neural network for validating segment points to enhance segmentation accuracy. Based on detailed analysis and comparison, it is observed that proposed approach enhances the cursive script segmentation accuracy with minimum computational complexity.

Alphabetical knowledge from whole words training: effects of explicit instruction and implicit experience on learning script segmentation

We investigated the possibility that pattern segmentation skills, specifically, phonological decoding, evolve implicitly in adult readers given training in an artificial script. In this Morse-like script each phoneme was represented by 2–3 discrete symbols. Subjects were trained in five consecutive sessions, on reading six nonsense words using a forced choice task that required translating symbol strings to sound patterns written in Latin letters. Three training conditions were compared within subject in terms of the time-course of learning and the ability to generalize the acquired knowledge (transfer): alphabetical whole words with letter decoding instruction (Explicit); alphabetical whole words (Implicit), and non-alphabetical whole words (Arbitrary). In separate blocks in each training session, a visual-matching task was administered using the same stimuli. Our results show: (a) that while all three training conditions were equally effective in terms of magnitude and time-course of learning accurate translation, each training condition resulted in a different type of knowledge (i.e. differential transfer). (b) Declarative knowledge of letters evolved from training on whole words only in subjects with previous experience in Explicit training. However, even with declarative knowledge of the specific letters subjects did not develop general letter segmentation skills. (c) Contrary to the robust transfer of learning gains to different stimuli within a given task, there was no significant transfer across tasks indicating that the locus of learning was task dependent. Altogether our results suggest that even given explicit letter instruction, training on word decoding may result in letter recognition rather than in alphabetic segmentation skills.

Knowledge-based English cursive script segmentation

Knowledge concerning the structure of some prominent English letters, as well as the structural characteristics between background regions and character components is investigated as a novel approach to cursive script segmentation. First, connected components consisting of more than one character are split into sub-components based on their face-up or face-down background regions. Secondly, the over-segmented sub-components are merged into characters according to the knowledge of character structures and their joining characteristics. The algorithm achieved around 80% correct segmentation on a difficult database of unconstrained handwritten words.