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.

Handwritten and Machine Printed Text Separation in Document Images Using the Bag of Visual Words Paradigm

Abstract: In a number of types of documents, ranging from forms to archive documents and books with annotations, machine printed and handwritten text may be present in the same document image, giving rise to significant issues within a digitisation and recognition pipeline. It is therefore necessary to separate the two types of text before applying different recognition methodologies to each. In this paper, a new approach is proposed which strives towards identifying and separating handwritten from machine printed text using the Bag of Visual Words paradigm (BoVW). Initially, blocks of interest are detected in the document image. For each block, a descriptor is calculated based on the BoVW. The final characterization of the blocks as Handwritten, Machine Printed or Noise is made by a Support Vector Machine classifier. The promising performance of the proposed approach is shown by using a consistent evaluation methodology which couples meaningful measures along with a new dataset.

Compound words in speech recognition systems

Abstract: An automatic speech recognition system includes a recognition engine, a compound lexicon, and a compounder The recognition engine generates a recognition result having a sequence of recognized words representative of an input utterance The words in the recognition result include compound word components which may be combined to form compound words The engine uses a recognition vocabulary of words and a language model which, for a given position in the sequence of recognized words and for selected words in the recognition vocabulary, associates a likelihood of such word occurring at such position The compound lexicon contains a plurality of compound word components and component connecting links structured so that links are present between components that are more likely to occur together than to occur as separate words The compounder replaces adjacent words in the sequence of recognized words which have corresponding linked components in the lexicon with a concatenation of the corresponding linked components The probabilities in the language model minimize the likelihood of a component in the lexicon occurring in the recognition result without a corresponding linked component for concatenation

On-line Character Recognition for Handwritten Kannada Characters using Wavelet Features and Neural Classifier

Abstract: The rapid advancement of the computer industry has led to computerization of various fields throughout the world, including India. Great emphasis is given to facilitate the users to interact with the computers in their local languages. Users will be more comfortable to input the instructions by their handwriting rather than through the existing local languages-converted-English keyboards. Since Kannada language is very rich in alphabet, for keying-in most of the Kannada characters, a combination of key strokes has to be used in such converted keyboards. This is very tedious and requires considerable practice and effort. An obvious solution is to input the instructions by handwriting. This paper reports the details of an on-line character recognition system for the Kannada characters developed by the authors that extracts the novel wavelet features from the contour of character written using a digitizer tablet pen. The conventional feedforward multilayer neural network is used as classifier. The results obtained are most encouraging and the system can be extended to other similar Indian languages, particularly, Telugu.

Feature extraction techniques of online handwriting arabic text recognition

Abstract: Online recognition of Arabic handwritten text has been an ongoing research problem for many years. Generally, online text recognition field has been gaining more interest lately due to the increasing popularity of hand-held computers, digital notebooks and advanced cellular phones. Most of the online text recognition systems consist of three main phases which are preprocessing, feature extraction, and recognition phase. This paper compares between different techniques that have been used to extract the features of Arabic handwriting scripts in online recognition systems. Those techniques attempt to extract the feature vector of Arabic handwritten words, characters, numbers or strokes. This vector then will be fed into the recognition engine to recognize the pattern using the feature vector. The structure and strategy of those reviewed techniques are explained in this article. The strengths and weaknesses of using these techniques will also be discussed.

Combining structural and statistical features for the recognition of handwritten characters

Abstract: The authors present a feature vector for the recognition of handwritten characters which combines the strengths of both statistical and structural feature extractors. Thanks to a combination of seven complementary families of features (ranging from pure structural to pure statistical and including both local and global features), a complete description of the characters can be achieved thus providing a wide range of identification clues. The recognition system has been tested on three categories of handwritten characters: handwritten well-segmented digits extracted from the NIST Database, uppercase letters collected from US dead letter envelopes and graphemes generated by a handwritten cursive word segmentation performed on US address word images. We thus demonstrate in this paper that high recognition rates with very low substitution rates can he achieved by means of the same general-purpose structural/statistical feature based vector.