Character Segmentation Algorithm Research Articles

Segmentation Method for NaXi Pictograph Character Recognition

NaXi pictograph, which is the only hieroglyph in use is important for researching into the evolution of the character. The research on the recognition of printed NaXi pictograph characters is now just beginning. The major problem with machine printed NaXi pictograph character segmentation is the shape of the letter depending on its location in the word. In this paper, a machine printed NaXi pictograph character segmentation algorithm, which is based on the windows backtracking histogram and some thresholds, is presented. To establish two sample databases of NaXi pictograph text images, and use three thresholds methods separately to make the binarization processing about images. After the testing, Ostu’s algorithm is more suitable for the binarization processing about NaXi pictograph images. For this sample database, we separately employ the projection algorithm and windows backtracking algorithm for segmentation, and the experimental results show that the windows backtracking algorithm in this paper is better than the projection algorithm.

Off-Line Handwritten Jawi Character Segmentation Using Histogram Normalization And Sliding Window Approach For Hardware Implementation

The task of segmenting text into characters is a necessary preprocessing step in the development of most character recognition systems because incorrectly segmented characters are likely to be incorrectly recognized. The segmentation of off-line handwritten Jawi text poses a higher challenge due to its cursive nature and various writing styles. In this paper, histogram normalization and sliding windows are used for hardware implementation of real-time off-line handwritten Jawi script character segmentation. Existing algorithms for character segmentation are compared with the proposed method. The hardware design is presented along with justifications of the proposed approach. The main advantage of the proposed algorithm is its simple design which enables it to be implemented in hardware without requiring a large amount of resources. The character segmentation algorithm was implemented and the results show a 98% segmentation accuracy.

Text localization and character segmentation algorithms for automatic recognition of slab identification numbers

This paper describes application-oriented text localization and character segmentation algorithms in images. The target text in our application includes many unclear characters due to poor environment as well as the fact that their positions are variable in the images. Consequently, it is difficult to expect a high success rate when using existing text localization algorithms that have been developed for generic texts. Therefore, it is necessary to develop a new text localization algorithm. We propose (1) a coarse algorithm for detecting top and bottom boundaries, (2) a fitness function that is used to decide the true text among the text candidates, (3) two kinds of presegmentation algorithms for calculating the fitness function, and (4) a blank-detecting algorithm that determines whether the text is upside down or not. By the proposed algorithms, input upside-down text is rotated automatically without using any supervised or unsupervised learning methods; further, character segmentation can be done in the process of selecting the true text. To evaluate the algorithms, image data captured by the installed recognition system at Pohang Steel Company (POSCO) are used, and experimental results show that the proposed algorithms are fast and reliable.

Iterative Cross Section Sequence Graph for Handwritten Character Segmentation

The iterative cross section sequence graph (ICSSG) is an algorithm for handwritten character segmentation. It expands the cross section sequence graph concept by applying it iteratively at equally spaced thresholds. The iterative thresholding reduces the effect of information loss associated with image binarization. ICSSG preserves the characters' skeletal structure by preventing the interference of pixels that causes flooding of adjacent characters' segments. Improving the structural quality of the characters' skeleton facilitates better feature extraction and classification, which improves the overall performance of optical character recognition (OCR). Experimental results showed significant improvements in OCR recognition rates compared to other well-established segmentation algorithms.

Cursive Arabic Script Segmentation and Recognition System

AbstractCharacter segmentation is a necessary preprocessing step for character recognition in many OCR systems. It is an important step because incorrectly segmented characters will not be recognized correctly. The most difficult case in character segmentation is cursive script. The scripted nature of Arabic written language poses some high challenges for automatic character segmentation and recognition. The authors present a new Character Segmentation Algorithm (ACSA) of Arabic script. The developed segmentation algorithm yields the splitting up of isolated handwritten words in perfectly separated characters. It is based on topological rules, which are constructed at the feature extraction phase. To increase ACSA’s performances, it was combined it with an Arabic characters recognition system, RECAM.

Cursive Arabic Script Segmentation and Recognition System

Character segmentation is a necessary preprocessing step for character recognition in many OCR systems. It is an important step because incorrectly segmented characters will not be recognized correctly. The most difficult case in character segmentation is cursive script. The scripted nature of Arabic written language poses some high challenges for automatic character segmentation and recognition. The authors present a new Character Segmentation Algorithm (ACSA) of Arabic script. The developed segmentation algorithm yields the splitting up of isolated handwritten words in perfectly separated characters. It is based on topological rules, which are constructed at the feature extraction phase. To increase ACSA’s performances, it was combined it with an Arabic characters recognition system, RECAM.

A new algorithm for machine printed Arabic character segmentation

The major problem with machine printed Arabic character segmentation is the shape of the letter depending on its location in the word. In this paper, a new machine printed Arabic character segmentation algorithm, which is based on the vertical histogram and some rules, is presented. The rules which are based on, not only the structural characteristics between background regions and character components but also the characteristics of isolated Arabic characters, are used to check whether the sub-word includes only one character. Then we use the vertical histogram and some other rules to find real segmentation points. Finally, we split the sub-word at the segmentation points. The experimental results show that the algorithm achieved about 94% correct segmentation.

Segmentation-recognition algorithm for zip code field recognition

This paper describes a recognition algorithm for zip code field recognition. The algorithm consists of an initial character segmentation algorithm and a connected-numeral splitting algorithm. The initial character segmentation algorithm employs connected component analysis with component merge technique based on proximity. The numeral splitting algorithm consists of a slant splitting algorithm based on discriminant analysis and two postprocessing algorithms based on local shape analysis. The splitting algorithm is integrated with a statistical classifier to form a segmentation-recognition algorithm to resolve the ambiguity of connected numeral splitting. The performance is tested by recognition experiments on zip code fields collected from real USPS mail envelopes.

A character segmentation algorithm for mixed‐mode communication

AbstractIn the realization of a mixed‐mode communication, it is necessary before recognizing individual characters, to separate text and black and white figure regions and to extract efficiently the character region. Problems in such a procedure are the detection and correction of the inclination of a document, separation of contact characters, the merge of disconnected characters, and extraction of a letterhead. This paper describes the results of studies on such problems. The document considered is the English text image, containing binary figures and a letterhead. The basic idea is as follows. Connected regions are obtained by directional propagation and shrinking to merge figures. Then: (1) a thinning process is performed to detect the inclination angle of the input text; (2) the sizes of the connected regions and their relative locations are examined to extract the letterhead. Estimation of pitch is performed and statistical data about individual characters are used to separate contact characters or merge disconnected characters. The experiment was made for 10 and 12 pitch printed characters, and the correct extraction rate was 100 percent for individual characters, and 94.6 percent for a letterhead. Thus it was verified that the proposed method is useful in extracting the character region.