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.

Image character recognition using deep convolutional neural network learned from different languages

Abstract: This paper proposes a shared-hidden-layer deep convolutional neural network (SHL-CNN) for image character recognition. In SHL-CNN, the hidden layers are made common across characters from different languages, performing a universal feature extraction process that aims at learning common character traits existed in different languages such as strokes, while the final softmax layer is made language dependent, trained based on characters from the destination language only. This paper is the first attempt to introduce the SHL-CNN framework to image character recognition. Under the SHL-CNN framework, we discuss several issues including architecture of the network, training of the network, from which a suitable SHL-CNN model for image character recognition is empirically learned. The effectiveness of the learned SHL-CNN is verified on both English and Chinese image character recognition tasks, showing that the SHL-CNN can reduce recognition errors by 16–30% relatively compared with models trained by characters of only one language using conventional CNN, and by 35.7% relatively compared with state-of-the-art methods. In addition, the shared hidden layers learned are also useful for unseen image character recognition tasks.

Isolated word recognition using phoneme-like templates

Abstract: This paper describes new technique for use in a word recognition system. This recognition system is especially efffective in speaker-dependent large vocabulary word recognition based on multiple reference templates. In this system, word templates are represented as sequences of descrete phoneme-like (pseudo-phoneme) templates which are automatically determined from a training set of word utterances by a clustering technique. In speaker-dependent 641 city names word recognition experiments, 96.3% recognition accuracy was obtained using 256 phoneme-like templates.

Hybrid pen-input character recognition system based on integration of online-offline recognition

Abstract: Describes a handwritten character recognition system that integrates offline recognition requiring a bitmap image and online recognition involving an input pattern as a sequence of x-y coordinates. Offline recognition performs well for painted or overwritten patterns (for which online recognition would not be suited), whereas online recognition is suitable for very deformed patterns (for which offline recognition is not suited). Because each method has different recognition capabilities, the methods complement each other when integrated together. We have implemented a hybrid handwritten character recognition system in which the recognition results of the offline and online recognizer are integrated to create an improved product. After testing several integration methods for a handwritten character database, we found that the best method increased the recognition rate from 73.8% (offline) and 84.8% (online) to 87.6% (integrated).

Writer identification using edge-based directional probability distribution features for arabic words

Abstract: A system for writer identification based on Arabic handwritten words was built. First a database of words was gathered and used as a test base. Then, features vectors were extracted from writers' word images. Prior to feature extraction, normalization operations were applied to a word or text line. In this research, we studied the feature extraction and recognition operations on Arabic text, on the identification rate of writers. Since there is no well known database containing Arabic handwritten words for researchers to test, we built a new database of off-line Arabic handwriting text to be used for writer identification research. The proposed database is meant to provide training and testing sets for Arabic writer identification research. Arabic handwritten words were collected from 100 writers. We evaluated the performance of edge-based directional probability distributions as features and other features in Arabic writer identification.

Real-time recognition of handprinted text

Abstract: During the past 10 years, there has been considerable interest in the automatic recognition of handwritten and machine-printed symbols. Most of these studies have involved computer recognition of an already completed symbol; a few have involved special electronics for the analysis and recognition of characters or words as they are being written.