Characters Segmentation of Cursive Handwritten Words based on Contour Analysis and Neural Network Validation

This paper presents a robust algorithm to identify the letter boundaries in images of unconstrained handwritten word . The proposed algorithm is based on  vertical  contour  analysis.  Proposed  algorithm  is  performed  to  generate  presegmentation by analyzing the vertical contours from right to left. The unwanted segmentation  points  are  reduced  using  neural  network  validation  to  improve accuracy  of  segmentation.  The  neural  network  is  utilized  to  validate segmentation  points.  The  experiments  are  performed  on  the  IAM  benchmark database.  The  results  are  showing  that  the  proposed  algorithm  capable  to accurately locating the letter boundaries for unconstrained handwritten words

Handwritten character recognition using a gradient based feature extraction

Handwriting Recognition is the task of transforming a language that is represented in its spatial form of graphical marks into its symbolic representation. In Offline Handwriting Recognition, there are three steps: preprocessing of the image, segmentation of words into characters and recognition of the characters. In this thesis I implemented two methods for character recognition, which is the most important step in Offline Handwriting Recognition. The heart of character recognition is the features that are extracted from the character image. The accuracy of the classification of the character image depends on the quality of the features extracted from the image. The two methods presented in this thesis use two different types of features. One uses the connectivity features among various segments in a character image, and the other method uses the gradient feature at each pixel to construct the feature vectors. Both these methods are discussed in detail in the following chapters

Neural-based Solutions for the Segmentation and Recognition of Difficult Handwritten Words from a Benchmark Database

A new intelligent segmentation technique is proposed that can be used in conjunction with a neural classifier and a simple lexicon for the recognition of difficult handwritten words. The segmentation technique initially employs a heuristic algorithm which searches for structural features within handwritten word images. As a result, the algorithm over-segments each word. An Artificial Neural Network (ANN) trained with 32,034 segmentation points is then used to verify the validity of the segmentation points found. Following segmentation, character matrices from each word are extracted, normalised and then passed through a global feature extractor after which a second ANN trained with segmented characters is used for classification. These recognised characters are grouped into words and presented to a variable-length lexicon which utilises a string processing algorithm to compare and retrieve words with highest confidences. This research provides promising results for segmentation, charac..