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.

Optical Character Recognition using MATLAB

Abstract: Optical character recognition, usually abbreviated to OCR, is the mechanical or electronic translation of scanned images of handwritten, typewritten or printed text into machine-encoded text. It is widely used to convert books and documents into electronic files, to computerize a record-keeping system in an office, or to publish the text on a website. OCR makes it possible to edit the text, search for a word or phrase, store it more compactly, display or print a copy free of scanning artifacts, and apply techniques such as machine translation, text-to-speech and text mining to it. OCR is a field of research in pattern recognition, artificial intelligence and computer vision. OCR systems require calibration to read a specific font; early versions needed to be programmed with images of each character, and worked on one font at a time. "Intelligent “systems with a high degree of recognition accuracy for most fonts are now needed. Hence this paper attempts to develop an intelligent OCR system to store the documents in electronic form using Matlab

“i” — A novel algorithm for optical character recognition (OCR)

Abstract: Computer vision, artificial intelligence and pattern recognition have been important areas of research for a while in the history of electronics and image processing. Optical character recognition (OCR) is one of the main aspects of computer vision and has evolved greatly since its inception. OCR is a method in which readable characters are recognized from optical data obtained digitally. Many methodologies and algorithms have been developed for this purpose using different approaches. Here we present one such approach for OCR named “ i ”. Amongst all other OCR systems available, “ i ” aims at a high speed, simple, font independent and size independent OCR system based on a unique segment extraction technique. This algorithm can be used as a kernel for single alphabet detection within a complete OCR solution system without the need for any complex mathematical operations. The highlight of this methodology is that, it does not use any libraries or databases of image matrices to recognize alphabets, but it has a unique algorithm to recognize alphabets instead. This algorithm has been implemented in MATLAB 7.14.0.739 build R2012a on a test set of 500 images of text and an accuracy of 100% for three font families namely Arial, Times New Roman and cchas been obtained.

Handwritten Farsi/Arabic Word Recognition

Abstract: This paper presents a novel holistic handwritten Farsi /Arabic word recognition scheme in situation where we face with word rotation and scale change. Image words features are extracted by exploiting rotation and scale invariance characteristics of M-Band packet wavelet transform performed on polar transform version of images of handwritten Farsi/Arabic words. The extracted features construct a feature vector for each word image. This vector is employed in recognition phase by finding the similar words based on the least Mahalanobis distance of feature vectors. This scheme is robust against rotation and scaling. Experimental results, obtained from testing different handwritten texts with various orientations and scales, show that proposed scheme outperforms Fourier-wavelet and Zernike moments algorithms. The robustness of new scheme has been tested with images corrupted by Gaussian noise and compared with similar schemes. Experimental results show that the accuracy of our algorithm reaches 95.8 percents.

An online handwritten music score recognition system

Abstract: The objective of this study is to produce a system that would allow music symbols to be written by hand using a pen-based computer that would simulate the feeling of writing on sheets of paper and that would also accurately recognize the music symbols. To accomplish these objectives, the following methods are proposed: (1) Two features, time-series data and an image of a handwritten stroke, are used to recognize strokes; and (2) The strokes are combined, as efficiently as possible, and outputted automatically as a music symbol. As a result, recognition rates of 97.60% and 98.80% were obtained in tests with strokes and music symbols, respectively.