Because of large shape variations in human handwriting, recognition accuracy of cursive handwritten word is hardly satisfying using a single classifier. In this paper we introduce a framework to combine results of multiple classifiers and present an intuitive run-time weighted opinion pool (RWOP) combination approach for recognizing cursive handwritten words with a large size vocabulary. The in...

This paper presents a novel approach towards Indic handwritten word recognition using zone-wise information. Because of complex nature due to compound characters, modifiers, overlapping and touching, etc., character segmentation and recognition is a tedious job in Indic scripts (e.g. Devanagari, Bangla, Gurumukhi, and other similar scripts). To avoid character segmentation in such scripts, HMMb...

This paper describes techniques to separate a line of unconstrained (written in a natural manner) handwritten text into words. When the writing style is unconstrained, recognition of individual components may be unreliable so they must be grouped together into word hypotheses, before recognition algorithms (which may require dictionaries) can be used. Our system uses original algorithms to dete...

The "many-to-many" hypothesis proposes that visual object processing is supported by distributed circuits that overlap for different object categories. For faces and words the hypothesis posits that both posterior fusiform regions contribute to both face and visual word perception and predicts that unilateral lesions impairing one will affect the other. However, studies testing this hypothesis ...

This paper develops word recognition methods for historical handwritten cursive and printed documents. It employs a powerful segmentation-free letter detection method based upon joint boosting with histogram-of-gradients features. Efficient inference on an ensemble of hidden Markov models can select the most probable sequence of candidate character detections to recognize complete words in ambi...

Abstract— This paper presents a new offline handwritten Devanagari word recognition system. Though Devanagari is the script for Hindi, which is the official language of India, its character and word recognition pose great challenges due to large variety of symbols and their proximity in appearance. In order to extract features which can distinguish similar appearing words, we employ Curvelet Tr...

The thesis addresses the application of multiple classifier systems (MCS) methods in the domain of handwriting recognition. To evaluate the MCS methods two different state-of-the-art handwritten word recognizers are used. Both recognizers are based on Hidden Markov Models (HMMs) and process handwritten words in the same manner: First the word image is normalized in respect to slant, skew and he...

There are lots of historical handwritten documents with information that can be used for several studies and projects. The Document Image Analysis and Recognition community is interested in preserving these documents and extracting all the valuable information from them. Handwritten word-spotting is the pattern classification task which consists in detecting handwriting word images. In this wor...

This paper presents a system that is being developed for the recognition of the handwritten legal amount in Brazilian bank checks. Our strategy used to approach the handwritten legal amount recognition problem puts on evidence the key-words: "mil", "reais/real", "centavos/centavo" which are almost always present in each amount. The recognizer, based on Hidden Markov Models, does a global word a...

This report describes analogic algorithms used in the preprocessing and segmentation phase of off-line handwriting recognition tasks. A segmentation based handwriting recognition approach is discussed i.e. the system attempts to segment the words into their constituent letters. In order to improve their speed, the utilized CNN algorithms, whenever possible, use dynamic, wave front propagationba...