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.

Recognition of Multi-oriented, Multi-sized, and Curved Text

Abstract: Text recognition is difficult from documents that contain multi-oriented, curved text lines of various character sizes. This is because layout analysis techniques, which most optical character recognition (OCR) approaches rely on, do not work well on unstructured documents with non-homogeneous text. Previous work on recognizing non-homogeneous text typically handles specific cases, such as horizontal and/or straight text lines and single-sized characters. In this paper, we present a general text recognition technique to handle non-homogeneous text by exploiting dynamic character grouping criteria based on the character sizes and maximum desired string curvature. This technique can be easily integrated with classic OCR approaches to recognize non-homogeneous text. In our experiments, we compared our approach to a commercial OCR product using a variety of raster maps that contain multi-oriented, curved and straight text labels of multi-sized characters. Our evaluation showed that our approach produced accurate text recognition results and outperformed the commercial product at both the word and character level accuracy.

Lexicon-driven HMM decoding for large vocabulary handwriting recognition with multiple character models

Abstract: This paper presents a handwriting recognition system that deals with unconstrained handwriting and large vocabularies. The system is based on the segmentation-recognition paradigm where words are first loosely segmented into characters or pseudocharacters and the final segmentation is obtained during the recognition process, which is carried out with a lexicon. Characters are modeled by multiple hidden Markov models (HMMs), which are concatenated to build up word models. The lexicon is organized as a tree structure, and during the decoding words with similar prefixes share the same computation steps. To avoid an explosion of the search space due to the presence of multiple character models, a lexicon-driven level building algorithm (LDLBA) is used to decode the lexical tree and to choose at each level the more likely models. Bigram probabilities related to the variation of writing styles within the words are inserted between the levels of the LDLBA to improve the recognition accuracy. To further speed up the recognition process, some constraints are added to limit the search efforts to the more likely parts of the search space. Experimental results on a dataset of 4674 unconstrained words show that the proposed recognition system achieves recognition rates from 98% for a 10-word vocabulary to 71% for a 30,000-word vocabulary and recognition times from 9 ms to 18.4 s, respectively.

Offline Handwritten Character Recognition of Gujrati script using Pattern Matching

Abstract: during the last forty years, Handwritten Character Recognition (HCR) has most often been investigated under the framework of Character Recognition (OCR) and Pattern Recognition. HCR is more considered as a perceptual and interpretation task closely connected with research into Human Language. India is a country which uses many languages in the different parts of the country be it for personal use or use of business. In this study we propose a novel solution for performing character recognition in Gujrati, the official language of Gujarat. Pursued by the preprocessing techniques, we suggest a method called Pattern Matching where a character is identified by analyzing its shape and comparing its features that distinguish each character. Various handwritten characters from forms or peripheral devices etc. are recognized with the help of various pre-processing and image enhancement techniques. These characters are further more specifically recognized by Pattern matching using Neural Network.

Recognizing handwritten text

Abstract: Notebook computers, using stylus input, are currently a hot topic among PC manufacturers. Handwriting recognition may be an important component of such systems, but only if everyday sloppy handwriting can be accommodated. If recognizes require unnaturally neat or boxed character input, such systems may fail in the marketplace. Neural nets have shown excellent performance at handwriting recognition. I present three neural net approaches to recognizing lines of English texti one using 2D image input, one using stroke sequence input, and one using context to combine the outputs of the other two networks. These networks can be combined to form a recognition engine that will handle natural lines of handwritten English text, including handprint, cursive script, and mixtures of both.