Segmentation of off-line cursive handwriting using linear programming☆

Knowledge-based English cursive script segmentation

We present a system for recognizing off-line, cursive, English text, guided in part by global characteristics (style) of the handwriting. We introduce a new method for segmenting words into letters, based on minimizing a cost function. Segmented letters are normalized with a novel algorithm that scales different parts of a letter separately removing much of the variation in the writing. We use a neural network for letter recognition and use the output of the network as posterior probabilities of letters in the word recognition process. We found that using a hidden Markov Model for word recognition is less successful than assuming an independent process for our small set of test words. In our experiments with several hundred words, written by 7 writers, 96% of the test words were correctly segmented, 52% were correctly recognized, and 70% were in the top three choices. >

Recognizing off-line cursive handwriting

Character segmentation (i.e., splitting the images of handwritten words into pieces corresponding to single letters) is one of the required steps in numerous off-line cursive handwritten word recognition solutions. It is also a very important step, because improperly extracted characters are u

Oversegmentation Methods for Character Segmentation in Off-Line Cursive Handwritten Word Recognition - An Overview

Segmentation of the handwritten Arabic characters is still one of the most difficult problems to develop a reliable Arabic OCR. This paper presents a complete Arabic OCR system that uses histogram clustering method for the segmentation of the Arabic word. This method gives the ability to process different user styles, and manages the variability of pen strokes. Also, a new algorithm for separating overlapped characters was proposed to support the proposed technique for segmentation. The feature extraction process was based on a combination between the PCA network and characters geometric features. A classifier for hundred of Arabic character images was designed using a decision tree induction algorithm, and MLP network. A segmentation correctness of 96% was achieved while the recognition rate of the whole system was 91.5%.

Histogram clustering and hybrid classifier for handwritten Arabic characters recognition

Describes a document reading system incorporating a pre-segmenter. The output from the pre-segmenter is passed into the character recognition stage of the document reader, which attempts classification of the objects between each pair of character boundaries within a joined group of characters. The combination of boundaries which achieves the highest overall confidence level for the whole group of joined characters is chosen to be the correct one. In addition, post-processing may be used to choose between the most likely interpretations of the words. This may simply take the form of a spelling checker correction system, or may be fed back into the earlier stages of the document reading system to request or suggest new segmentation positions or alternative character classifications.

https://ieeexplore.ieee.org/iel3/1975/5142/00198761.pdf

P.D. Friday

Papers

Isolating individual handwritten characters