Handwritten characters are seen everywhere in our day-to-day life. Almost all the things we do involve letters, from writing cheques to notes manually. character recognition is considered as a core diversity of emerging application by using concepts machine learning. It used widely for performing practical applications such reading computerized bank cheques. However, executing system carry out ...

The paper describes a recognition scheme for reading handwritten cursive words using three word recognition techniques. It particularly focuses on the implementation used to combine the three techniques based on a comparative sru& of different strategies. The first holistic recognition technique derives a global encoding of the word. The other techniques both rely on the segmentatiorr of the wo...

Word extraction is one of the most critical steps in handwritten recognition systems. It challenging for many reasons, such as variability writing styles, touching and overlapping characters, skewness problems, diacritics, ascenders, descenders' presence. In this work, we propose a deep-learning-based approach Arabic word extraction. We used an Attention-based CNN-ConvLSTM (Convolutional Long S...

Although several studies have focused on recognition of individual language, no attempt has been seriously made for online recognition of handwritten script in multiple languages. In this paper, a network-based approach is proposed for recognizing sequences of words in multiple languages. Viewing handwritten script as an alternating sequence of words and interword ligatures, a hierarchical hidd...

The study of multiple classifier systems has become an area of intensive research in pattern recognition recently. Also in handwriting recognition, systems combining several classifiers have been investigated. In this paper new methods for the creation of classifier ensembles based on feature selection algorithms are introduced. Those new methods are evaluated and compared to existing approache...

In this paper we have developed a system that can automatically discriminate between machine-printed and handwritten words in structured bi-lingual (Arabic and French) form document layout. Our system has been applied in the context of Tunisian National Health Insurance Fund for medical care costs refund with encouraging results. In the used forms, handwritten data usually touch or cross the pr...

In this paper we introduce a new connectionist approach to on-line handwriting recognition and address in particular the problem of recognizing handwritten whiteboard notes. The approach uses a bidirectional recurrent neural network with long short-term memory blocks. We use a recently introduced objective function, known as Connectionist Temporal Classification (CTC), that directly trains the ...

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 ...

The study of multiple classifier systems has become an area of intensive research in pattern recognition recently. Also in handwriting recognition, systems combining several classifiers have been investigated. In this paper new methods for the creation of classifier ensembles based on feature selection algorithms are introduced. Those new methods are evaluated and compared to existing approache...

This paper describes an approach to combine neural network (NN) and Hidden Markov models (HMM) for solving handwritten word recognition problem. The preprocessing involves generating a segmentation graph that describes all possible ways to segment a word into letters. To recognize a word, the NN computes the observation probabilities for each letter hypothesis in the segmentation graph. The HMM...