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.

Multi-script handwriting recognition with FOHDEL

Abstract: Until now handwritten character recognition systems used script specific methodologies. We present a unified syntactic approach for multi-script recognition. The fuzzy pattern description language FOHDEL is used to store fuzzy features in the form of fuzzy rules. First we briefly describe the proposed recognition methodology for Latin, Devanagari and Kanji scripts by analyzing their characteristic properties. Further we present the main features of FOHDEL by a comparative rule generation of three scripts under experiment. Finally the system integration of the proposed multi-script recognition scheme in the existing Latin recognition system is presented.

Off-line cursive handwritten Tamil character recognition

Abstract: In spite of several advancements in technologies pertaining to Optical character recognition, handwriting continues to persist as means of documenting information for day-to-day life. The process of segmentation and recognition pose quiets a lot of challenges especially in recognizing cursive handwritten scripts of different languages. The concept proposed is a solution crafted to perform character recognition of hand-written scripts in Tamil, a language having official status in India, Sri Lanka, and Singapore. The approach utilizes discrete Hidden Markov Models (HMMs) for recognizing off-line cursive handwritten Tamil characters. The tolerance of the system is evident as it can overwhelm the complexities arise out of font variations and proves to be flexible and robust. Higher degree of accuracy in results has been obtained with the implementation of this approach on a comprehensive database and the precision of the results demonstrates its application on commercial usage. The methodology promises to present a simple and fast scaffold to construct a full OCR system extended with suitable pre-processing.

HMM based handwritten text recognition using biometrical data acquisition pen

Abstract: Development of new text and graphical input devices is considered to be important part of human-computer interaction by many researchers worldwide. The paper presents our experience with the text recognition methods that we have developed for a new designed electronic pen that produces signals corresponding to the movement of the pen on paper. Signals are described by a set of primitives and hidden Markov models are used for word recognition. Results of tests are discussed as well as other possible application areas of our electronic pen.

Voice recognition apparatus, voice recognition method and recording medium

Abstract: A voice recognition apparatus includes an extraction unit extracting a feature amount from a voice signal, a word dictionary storing a plurality of recognition words; a reject word generation unit storing reject words in the word dictionary in association with the recognition words and a collation unit calculating a degree of similarity between the voice signal and each of the recognition words and reject words stored in the word dictionary by using the feature amount extracted by the extraction unit, determining whether or not a word having a high calculated degree of similarity corresponds to a reject word, when the word is determined as the reject word, excluding the recognition word stored in the word dictionary in association with the reject word from a result of recognition, and outputting a recognition word having a high calculated degree of similarity as a result of recognition.

Character prototype selection for handwriting recognition in historical documents

Abstract: Handwriting recognition in historical documents is vital for making scanned manuscript images amenable to searching and browsing in digital libraries. A valuable source of information is given by the basic character shapes that vary greatly for different manuscripts. Typically, character prototype images are extracted manually for bootstrapping a recognition system. This process, however, is time-consuming and the resulting prototypes may not cover all writing styles. In this paper, we propose an automatic character prototype selection method based on a forced alignment using Hidden Markov Models (HMM) and graph matching. Besides the predominant character shape given by the median or center graph, structurally different additional prototypes are retrieved with spanning and k-centers prototype selection. On the historical Parzival data set, it is demonstrated that the proposed automatic selection outperforms a manual selection for handwriting recognition with graph similarity features.