Segmentation Of Handwritten Words Research Articles

Character Segmentation and Recognition of Marathi Language

Abstract: In this paper Segmentation is one the most important process which decides the success of character recognition fashion. Segmentation is used to putrefy an image of a sequence of characters into sub images of individual symbols by segmenting lines and words. In segmentation image is partitioned into multiple corridor. With respect to the segmentation of handwritten words into characters it's a critical task because of complexity of structural features and kinds in writing styles. Due to this without segmentation these touching characters, it's delicate to fete the individual characters, hence arises the need for segmentation of touching characters in a word. Then we consider Marathi words and Marathi Numbers for segmentation. The algorithm is use for Segmentation of lines and also characters. The segmented characters are also stores in result variable. First it Separate the lines and also it Separate the characters from the input image. This procedure is repeated till end of train. Keywords: Image Segmentation, Handwritten Marathi Characters, Marathi Numbers, OCR.

Holistic Persian Handwritten Word Recognition Using Convolutional Neural Network

Due to the cursive-ness and high variability of Persian scripts, the segmentation of handwritten words into sub-words is still a challenging task. These issues could be addressed in a holistic approach by sidestepping segmentation at the character level. In this paper, an end-to-end holistic method based on deep convolutional neural network is proposed to recognize off-line Persian handwritten words. The proposed model uses only five convolutional layers and two fully connected layers for classifying word images effectively, which can lead to a substantial reduction in parameters. The effect of various pooling strategies is also investigated in this paper. The primary goal of this article is to ignore handcrafted feature extraction and attain a generalized and stable word recognition system. The presented model is assessed using two famous handwritten Persian word databases called Sadri and IRANSHAHR. The recognition accuracies were obtained at 98.6% and 94.6%, on Sadri and IRANSHAHR datasets respectively, and outperformed the state-of-the-art methods.

Segmentation of handwritten words into characters

In this paper, SFF (Segmentation Facilitate Feature) technique is proposed to find the junction path to segment touched components based on the seed pixel selected among candidate pixels. Handwritten Recognition system has number of applications like reading postal address, filling forms, reading bank cheques, offering several challenges. In practice, constitute of the word images get touched in handwritten data due to variability in stroke, shortage of space which make the individual character extraction from the word image more complicated. Segmentation of individual in a word image requires a technique that takes care of the variability of writing. This paper proposed the SFF (Segmentation Facilitate Feature) technique to find seed pixel among candidate pixels based on 3-neighbouring pixels. It is used to find junction pixels which form a junction path to segregate the touched component. The junction path is selected to avoid the issues arising due to artifacts or deletion of components features. For experimentation, 1840 legal amount words containing touching components are used. The above number includes 250 words from benchmark database (ICDAR) and 1590 words are gathered from 15 different writers. On implementing, SFF (Segmentation Facilitate Feature) technique on the above mentioned database, 89.9% accuracy is achieved and a higher accuracy level 96.2% is achieved when performed on 1000 words containing two touching consonants.

H‐WordNet: a holistic convolutional neural network approach for handwritten word recognition

Segmentation of handwritten words into isolated characters and their recognition are challenging due to the presence of high variability and cursiveness in Indian scripts. The complex shapes and availability of numerous atomic character classes, compound characters, modifiers, ascendants, and descendants make the recognition task even more difficult. A holistic approach effectively tackles such issues by avoiding the character-level segmentation and the earlier holistic methods have been mostly developed using multi-stage machine learning architecture. In this study, a deep convolutional neural network-based holistic method termed ‘H-WordNet’ is proposed for handwritten word recognition. The H-WordNet model includes merely four convolutional layers and one fully connected layer to effectively classify the word images', which lead to a significant reduction in parameters. The efficacy of different pooling operations with the proposed model is investigated. The main purpose of this study is to avoid the need for handcrafted feature extraction and obtain a more stable and generalised system for word recognition. The proposed model is evaluated using a standard handwritten Bangla word database (CMATERdb2.1.2), which contains 18000 Bangla word images of 120 different categories and it obtained a higher recognition accuracy of 96.17% when compared to recent state-of-the-art methods.

Segmentation of handwritten words using structured support vector machine

Words and characters segmentation is a most indispensable and fundamental task for the handwritten script recognition. However, the complex language structures, deviation in pen breadth and slant in inscription make the feature extraction process very challenging. In this research, a binary quadratic process has been formulated for the word segmentation. It deliberates a co-relationship between the inter-word gap and intra-word gap. The structured support vector machine is used for the experiment. Experimental results of public datasets (i.e., ICDAR2009 and ICDAR2013) show state-of-the-art performance of the designed algorithm.

Segmentation of Touching Arabic Characters in Handwritten Documents by Overlapping Set Theory and Contour Tracing

Segmentation of handwritten words into characters is one of the challenging problem in the field of OCR. In presence of touching characters, make this problem more difficult and challenging. There are many obstacles/challenges in segmentation of touching Arabic handwritten text. Although researches are busy in solving the problem of segmentation of these touching characters but still there exist unsolved problems of segmentation of touching offline Arabic handwritten characters. This is due to large variety of characters and their shapes. So in this research, a new method for segmentation of touching Arabic Handwritten character has been developed. The main idea of the proposed method is to segment the touching characters by identifying the touching point by overlapping set theory and ending points of the Arabic word by applying some standard morphology operation methods. After identifying all the points, segmentation method is applied to trace the boundaries of characters to separate these touching characters. Experiments were conducted on touching characters taken from different data sets. The results show the accuracy of the proposed method.

Extraction of Characters and Modifiers from Handwritten Gujarati Words

The research activity related to Optical Character Recognition (OCR) for almost all Indian languages is very less. Gujarati script is one of the scripts for which very less literature is available, as far as OCR activities are concerned. This paper describes one of the important phase of OCR, segmentation of handwritten words into its basic components namely basic characters, conjunct characters and modifiers, which are essential for recognition of a word. The paper describes methods for identification of zone boundaries for a word and usage of zone boundaries details for segmenting the word into its subcomponents. Connected component labeling is applied to detect subcomponents of a word, which can be further dissected if needed to obtain other subcomponents of word. It is the first attempt to dissect handwritten Gujarati words into its subcomponents.

Isolated Handwritten Words Segmentation Techniques in Gurmukhi Script

Segmentation of handwritten words is a challenging task primarily because of structural features of the script and varied writing styles. Handwritten words are also prone to the problem of overlapped, connected, merged and broken characters. Based on certain properties of Gurmukhi script, different zones across the height of word are detected. Segmentation accuracy of 72.6% has been achieved with the use of the algorithms for segmenting all types of words. Segmentation accuracy of 88.1% has been achieved for segmenting all types of handwritten words in Gurmukhi script. Further, different categories of overlapping and touching characters in all the three zones (upper, middle and lower zone) of handwritten words in Gurmukhi script have been identified on the basis of structural properties of Gurmukhi script. A method for segmenting overlapping characters in middle zone has been proposed.