Recognizing Cursive Typewritten Text Using Segmentation-Free System

Recognizing Cursive Typewritten Text Using Segmentation-Free System

Feature extraction plays an important role in text recognition as it aims to capture essential characteristics of the text image. Feature extraction algorithms widely range between robust and hard to extract features and noise sensitive and easy to extract features. Among those feature types are statistical features which are derived from the statistical distribution of the image pixels. This p...

Cursive Character Segmentation Using Neural Network Techniques

Cursive Handwriting Segmentation Using Ideal Distance Approach

Received Jan 11, 2017 Revised Apr 7, 2017 Accepted Jun 11, 2017 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 recogniti...

Morphological Cursive Word Segmentation

This work describes a new algorithm for segmentation of handwritten cursive words, based on the following two hypothesis: 1) there exists natural segmentation points (SPs) corresponding to disconnected characters; and 2) the physical SPs between connected characters are located in ligatures. The morphological operations of opening and closing are utilized in the determination of word ligatures....

Region growing based segmentation algorithm for typewritten and handwritten text recognition

This paper presents a new technique of high accuracy to recognize both typewritten and handwritten English and Arabic texts without thinning. After segmenting the text into lines (horizontal segmentation) and the lines into words, it separates the word into its letters. Separating a text line (row) into words and a word into letters is performed by using the region growing technique (implicit s...