Segmentation Of Touching Characters Research Articles

The Trend of Segmentation for Arabic Handwritten Touching Characters

The paper is a comprehensive study of existing research trends in the sector of Arabic language, with a focus on state-of-the-art methods to illustrate the existing condition of various theory in that sector, with the goal of facilitating the adaptation and extension of prior ones into new systems and applications. In the Arabic alphabet, there are 28 letters. Depending on its place in the word, every Arabic letter has over one shape; a single character may have from one to four shapes. The Touching between character and the Overlapping occurred in the handwritten. Historical documents contained a massive knowledge and culture. There are many old books that need to be converted into readable format. Which would take a long time if humans converted it. However, the main problem is the lack of research in Arabic Handwritten especially for segmentation of touching characters. Thus, current trends of the segmentation techniques are investigated to identify the current state-of-the art of segmenting touching characters in other domains for constructing enhance techniques for Arabic touching characters. In this paper, it reviewed approaches for the segmentation of the touching characters. This paper presents the trend of approaches for the recognition process and segmentation of Arabic handwritten touching characters. In this paper, it highlighted the strength of each technique, the method used, and the drawback of the techniques. Based on the outcome, this will provide a good foundation for constructing a better technique for segmentation of Arabic touching characters, especially from the degraded documents.

Optimal Character Segmentation for Touching Characters in Tamil Language Palm Leaf Manuscripts using Horver Method

An optimality of an automatic character recognition for Tamil palm leaf manuscripts can be achieved only by an efficient segmentation of touching characters. In this article, the touching characters are segmented as a single character to achieve an optimum solution by the recognizer in Optical Character Recognition (OCR). The proposed method provides a novelty in touching character segmentation of Tamil palm leaf manuscripts. An initial process of separation of background image and foreground characters is applied on the palm leaf images by filtering and removing unwanted pieces of characters by noise removal methods. The thickening process overcomes the difficulty of small breakages in the characters. The aspect ratio of the character image can be used to categorize the character such as single or multi touching. Single touching is divided by yet another ways such as horizontal or vertical touching. Finally, the proposed algorithm for Horizontal and Vertical character segmentation named as HorVer method is applied on the horizontally and vertically touching characters to segment as independent character. Experimental result produces 91% of an accuracy on segmenting the touching characters in Tamil palm leaf manuscript images collected from various resources and Tamil Heritage Foundation (THF). A novelty method can be achieved in Tamil touching character segmentation by the proposed algorithm.

Drop flow method: an iterative algorithm for complete segmentation of Devanagari ancient manuscripts

One of the major challenges of ancient manuscripts recognition is character segmentation. Because of many distinct features of ancient documents (thick characters, overlapping and touching characters), character segmentation is a very difficult task. Devanagari ancient manuscripts consist of vowels, consonants, modifiers, conjuncts and compound characters. Using existing techniques, segmentation of overlapping and touching characters is problematic. In this paper, an iterative character segmentation algorithm is presented for ancient documents in Devanagari script. At the beginning, the lines are extracted from the ancient documents by dividing the document image into vertical stripes and then using piecewise horizontal projection profiles. After that, these lines are segmented into words using vertical projection profiles and finally, words are segmented in characters using an iterative algorithm. In each iteration, character segmentation is refined. In the present work, we have proposed a new algorithm with the name ‘Drop Flow Method’ to find the segmentation path between touching components. The proposed algorithm can segment touching characters and 96.0% accuracy has been achieved for complete segmentation of Devanagari ancient manuscripts.

A fuzzy approach to segment touching characters

Abstract Despite many years of research, correct and reliable segmentation of touching characters is still a hard task to solve. In the recent years, many methods and algorithms have been proposed; nevertheless the problem is still open. In this paper, we propose a novel method based on fuzzy logic that combines three different techniques to segment touching characters. These techniques have already been used in other studies but they have never been used all together. We propose a 3–input/1–output fuzzy inference system with fuzzy rules that are specifically optimized to segment touching Latin characters. The method is applicable to both printed and handwritten characters. We discuss the performances of our method by comparing it with state of the art. Results show that our method provide a better accuracy to segment characters even with noisy touching characters.

Segmentation of Degraded Text using Dynamic Profile Projection in Handwritten Gurmukhi Script

Character segmentation is a vital area of research for optical character recognition. The process of OCR involves several steps to recognize the character. After converting the scanner's output as bitmap image, set its threshold value. The recogni tion phase's output is totally depends upon the segmentation phase. Touching and broken character affects the accuracy rate of the recognition phase badly. Existing methods concentrate only upon the single touching characters problem with fixed size and mostly works upon th e middle zone. Segmentation of the broken character is an uphill task. In this paper, we used proposed technique for identification and segmentation of multiple touching characters in handwritten Gurmukhi words. In proposed technique firstly scanner's output is saved as bitmap image and then set its threshold value 200. The bitmap image is segmented into individual characters, called segments after applying the dynamically projection technique. The name of developed method i.e. Dynamic profile projection technique means t hat it works for variable size. Thus, the new technique works upon the segmentation of broken, multiple touching characters of variable size including the three zones of the handwritten gurmukhi script and increase the accuracy for touching characters. Thus, after the implementation of this concept got encouraging results than the existing systems.

A Review on Segmentation of Touching and Broken Characters for Handwritten Gurmukhi Script

Character Segmentation of Handwritten Documents has been an interested area of research and its applicable environment becomes it a challenging research topic. The desire to edit the scanned document leads to develop the idea of optical character recognition. Segmentation plays very important role in optical character recognition system. The incorrect segmentation is just like a garbage in and garbage out. Segmentation of the broken character is quite difficult because vertical profile projection technique assumes the broken parts of the characters as individual characters. Existing methods focuses only upon the single touching characters. But our main focus is to design a robust method for the segmentation of broken and multiple touching characters. Existing systems focus only on the segmentation of fixed sized characters. But we develop the size independent algorithm which works on variable size characters. Thus, in the proposed method we develop the algorithm which works on the segmentation of broken, multiple touching characters of independent size including the three zones of the handwritten gurmukhi script. The main challenges like as variation in handwriting style etc. make the segmentation to difficult. General Terms Optical Character Recognition

Multi-oriented touching text character segmentation in graphical documents using dynamic programming

The touching character segmentation problem becomes complex when touching strings are multi-oriented. Moreover in graphical documents sometimes characters in a single-touching string have different orientations. Segmentation of such complex touching is more challenging. In this paper, we present a scheme towards the segmentation of English multi-oriented touching strings into individual characters. When two or more characters touch, they generate a big cavity region in the background portion. Based on the convex hull information, at first, we use this background information to find some initial points for segmentation of a touching string into possible primitives (a primitive consists of a single character or part of a character). Next, the primitives are merged to get optimum segmentation. A dynamic programming algorithm is applied for this purpose using the total likelihood of characters as the objective function. A SVM classifier is used to find the likelihood of a character. To consider multi-oriented touching strings the features used in the SVM are invariant to character orientation. Experiments were performed in different databases of real and synthetic touching characters and the results show that the method is efficient in segmenting touching characters of arbitrary orientations and sizes.

ON SEGMENTATION OF TOUCHING CHARACTERS AND OVERLAPPING LINES IN DEGRADED PRINTED GURMUKHI SCRIPT

Character segmentation plays a very important role in a text recognition system. The simple technique of using inter-character gap for segmentation is useful for fine printed documents, but this technique fails to give satisfactory results if the input text contains touching characters. In this paper, we have proposed two algorithms to segment touching characters, and one algorithm to segment overlapping lines in degraded printed Gurmukhi document. Various categories of touching characters in different zones, along with their solutions, have been proposed. The solution methodology extensively uses the structural properties of Gurmukhi script. The algorithm proposed for segmenting horizontally overlapping lines uses a heuristics based upon the height of a character. The problem of multiple horizontally overlapping lines may occur in a number of situations such as printed newspapers, old magazines and books etc. Similarity among Indian scripts allows us to use these algorithms for solving the segmentation problems in other Indian languages also.

A Model-Based Segmentation Method for Handwritten Numeral Strings

Segmentation of handwritten touching characters is an important task in optical character recognition (OCR). A new model-based segmentation algorithm is proposed for handwritten numeral strings. This method is based on the boundary analysis of the numeral strings, where a set of new features are extracted. These features can be used for global interpretation of the boundary structures. Various models are then constructed for the common touching patterns, which can increase the reliability of the segmentation and reduce the segmentation time. While we concentrate on the description of the segmentation method for two single-touching digits in this paper, our method can be extended to segmentation of touching digits in other situations.

Segmentation of touching characters in printed document recognition

A new discrimination function is presented for segmenting touching characters based on both pixel and profile projections. A dynamic recursive segmentation algorithm is developed for effectively segmenting touching characters. Contextual information and spell checking are used to correct errors caused by incorrect recognition and segmentation. Based on 12 real documents, a maximum 99.85% and a minimum 99.4% recognition accuracy is achieved.