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.

Speech recognition system, speech recognition result output method, and speech recognition result output program

Abstract: A speech recognition system in which, even when the user makes an utterance including a word that satisfies a predetermined condition such as an unknown word, such a fact can be presented to the user, and the user can confirm the fact easily, is provided. The speech recognition system includes a word speech recognition section that converts input speech to a recognition result word sequence by using a predetermined word dictionary for recognition, a syllable recognition section that converts input speech to a recognition result syllable sequence, a segment determination section that determines a segment that corresponds to a predetermined condition which is a ground for estimating that a word in the converted recognition result word sequence is an unknown word, and an output section that obtains a partial syllable sequence from the recognition result syllable sequence corresponding to the determined segment, and outputs one or more word entries, which are in the vicinity of a position at which the partial syllable sequence is arranged in the word dictionary for recognition in which words are arranged in the order defined for word entries, together with the recognition result word sequence.

Determination of the number of writing variants with an HMM based cursive word recognition system

Abstract: An important parameter for building a cursive script model is the number of different, relevant letter writing variants. An algorithm performing this task automatically by optimizing the number of letter models in an HMM-based script recognition system is presented. The algorithm iteratively modified selected letter models; for selection, quality measures like HMM distance and emission weight entropy are developed, and their correlation with recognition performance is shown. Theoretical measures for the selection of overall model complexity are presented, but best results are obtained by direct selection criteria: likelihood and recognition rate of training data. With the optimized models, an average improvement in recognition rate of up to 5.8 percent could be achieved.

Turkish handwritten text recognition: a case of agglutinative languages

Abstract: We describe a system for recognizing unconstrained Turkish handwritten text. Turkish has agglutinative morphology and theoretically an infinite number of words that can be generated by adding more suffixes to the word. This makes lexicon-based recognition approaches, where the most likely word is selected among all the alternatives in a lexicon, unsuitable for Turkish. We describe our approach to the problem using a Turkish prefix recognizer. First results of the system demonstrates the promise of this approach, with top-10 word recognition rate of about 40% for a small test data of mixed handprint and cursive writing. The lexicon-based approach with a 17,000 word-lexicon (with test words added) achieves 56% top-10 word recognition rate.

An intelligent scanner with handwritten odia character recognition capability

Abstract: Character recognition plays an important role in the modern world. It can solve more complex problems and make human's job easier. Difficulties in recognition of handwritten text in Indian scripts include extreme cursiveness in their handwritten form due to the presence of vowel modifiers and compound characters. Here we propose a simple yet robust structural solution for recognizing handwritten characters in Odia (the official language of Odisha, a state in Republic of India). In the proposed system, a given text is segmented into lines and then each line is segmented into individual words and then each word is segmented into individual characters or basic symbols. Basic symbols are identified as the fundamental units of segmentation used for recognition. All the characters are divided into two groups (Group-I and Group-II) according to the property i.e. whether a vertical line is present or absent at the right-most part. All the characters of the two groups are resized into 20×14 pixels, which are directly subjected to train the two neural networks (one for Group-I and another for Group-II). Using the proposed system we have found better result for proper recognition rate as compared to other methods. The proposed sensing system is also found to be efficient in compressing the script data quite efficiently.

Recognition of unconstrained legal amounts handwritten on Chinese bank checks

Abstract: This work presents a novel research investigation on legal amount recognition of unconstrained cursive handwritten Chinese character in the environment of A2iA CheckReader - a commercial bank check recognition system. The following problems and their solutions are described: character set of Chinese legal amounts, preprocessing (slant detection and correction), segmentation, feature extraction, grammar, automatic annotation of Chinese characters before and during training, and neural network/hidden Markov model training and recognition. The system is trained with 47.8 thousand real bank checks, and validated with 12 thousand real bank checks. The recognition rate at the character level is 93.5%, and the recognition rate at the legal amount level is 60%. This is the first successful commercial product in this domain.