Intelligent word recognition

About: Intelligent word recognition is a research topic. Over the lifetime, 2480 publications have been published within this topic receiving 45813 citations.

An application of SVM in character recognition with chain code

Abstract: Artificial intelligence, pattern recognition and computer vision has a significant importance in the field of electronics and image processing. Optical character recognition (OCR) is one of the main aspects of pattern recognition and has evolved greatly since its beginning. OCR is a system which recognized the readable characters from optical data and converts it into digital form. Various methodologies have been developed for this purpose using different approaches. In this paper, general architecture of modern OCR system with details of each module is discussed. We applied Moore neighborhood tracing for extracting boundary of characters and then chain rule for feature extraction. In the classification stage for character recognition, SVM is trained and is applied on suitable example.

A new database for research on bank-check processing

Abstract: This paper presents a new database for off-line handwriting recognition. The database, that is particularly devoted to research on bank-check recognition, up to now includes instances of isolated digits and characters, basic words of worded amounts, and signatures. Pattern images are stored using a standard image format, and hence they are easily usable by several commercial and scientific image processing packages.

Relative positioning of stroke-based clustering: a new approach to online handwritten devanagari character recognition

Abstract: In this paper, we propose a new scheme for Devanagari natural handwritten character recognition. It is primarily based on spatial similarity-based stroke clustering. A feature of a stroke consists of a string of pen-tip positions and directions at every pen-tip position along the trajectory. It uses the dynamic time warping algorithm to align handwritten strokes with stored stroke templates and determine their similarity. Experiments are carried out with the help of 25 native writers and a recognition rate of approximately 95% is achieved. Our recognizer is robust to a large range of writing style and handles variation in the number of strokes, their order, shapes and sizes and similarities among classes.

DRS: a workstation-based document recognition system for text entry

Abstract: Document recognition system (DRS), a workstation-based prototype document analysis system that uses optical character recognition (OCR), is described. The system provides functions for image capture, block segmentation, page structure analysis, and character recognition with contextual postprocessing, as well as a user interface for error correction. All the functions except image capture and character recognition have been implemented by means of software for the Japanese edition of OS/2. >

A hybrid feature and discriminant classifier for high accuracy handwritten Odia numeral recognition

Abstract: Unconstrained handwritten character recognition is a major research area where there is a lot of scope for improving accuracy. There are many statistical, structural feature extraction techniques being proposed for different languages. Many classifier models are combined with these features to obtain high recognition rates. There still exists a gap between the recognition accuracy of printed characters and unconstrained handwritten scripts. Odia is a popular and classical language of the eastern part of India. Though the research in Optical Character Recognition (OCR) has advanced in other Indian languages such as Devanagari and Bangla, not much attention has been given to Odia character recognition. We propose a hybrid feature extraction technique using Kirsch gradient operator and curvature properties of handwritten numerals, followed by a feature dimension reduction using Principal Component Analysis (PCA). We use Modified Quadratic Discriminant Function (MQDF), Discriminative Learning Quadratic Discriminant Function (DLQDF) classifiers as they provide high accuracy of recognition and compare both the classifier performances. We verify our results using the Odia numerals database of ISI Kolkata. The recognition accuracy for Odia numerals with our proposed approach is found to be 98.5%.