Cursive Handwriting Segmentation using Ideal Distance Approach

Offline cursive handwriting becomes a major challenge due to the huge amount of handwriting varieties such as slant handwriting, space between words, the size and direction of the letter, the style of writing the letter and handwriting with contour similarity on some letters. There are some steps for recursive handwriting recognition. The steps are preprocessing, morphology, segmentation, features of letter extraction and recognition. Segmentation is a crucial process in handwriting recognition since the success of segmentation step will determine the success level of recognition. This paper proposes a segmentation algorithm that segment recursive handwriting into letters. These letters will form words using a method that determine the intersection cutting point of image recursive handwriting with an ideal image distance. The ideal distance of recursive handwriting image is an ideal distance segmentation point in order to avoid the cutting of other letter’s section. The width and height of images are used to determine the accurate segmentation point. There were 999 recursive handwriting input images taken from 25 researchers used for this study. The images used are the images obtained from preprocessing step. Those are the images with slope correction. This study used Support Vector Machine (SVM) to recognize recursive handwriting. The experiments show the proposed segmentation algorithm able to segment the image precisely and have 97% success recognizing the recursive handwriting

Slant estimation and core-region detection for handwritten Latin words

In this paper, we present a new technique that estimates the slant in handwritten words while a new word core-region detection method is introduced as part of the proposed technique. The proposed core-region detection algorithm can be also used independently to detect the upper and lower baselines of a word. Our method takes advantage of the orientation of the non- horizontal strokes of Latin characters as well as their location regarding to the word's core-region. As a first step, the word core-region is detected with the use of novel reinforced horizontal black run profiles which permits to detect the core-region scan lines more accurately. Then, the near-horizontal parts of the document word are extracted and the orientation and the height of non-horizontal remaining fragments as well as their location in relation to the word's core-region are calculated. Word slant is estimated taking into consideration the orientation and the height of each fragment while an additional weight is applied if a fragment is partially outside the core-region of the word which indicates that this fragment corresponds to a part of the character stroke that has a significant contribution to the overall word slant and should by definition be vertical to the orientation of the word. Extensive experimental results prove the efficiency of the proposed slant estimation method compared to current state-of-the-art algorithms. © 2012 Elsevier B.V. All rights reserved