The examination of how we read handwritten words (i.e., the original form of writing) has typically been disregarded in the literature on reading. Previous research using word recognition tasks has shown that lexical effects (e.g., the word-frequency effect) are magnified when reading difficult handwritten words. To examine this issue in a more ecological scenario, we registered the participant...

ÐThis paper describes a hidden Markov model-based approach designed to recognize off-line unconstrained handwritten words for large vocabularies. After preprocessing, a word image is segmented into letters or pseudoletters and represented by two feature sequences of equal length, each consisting of an alternating sequence of shape-symbols and segmentationsymbols, which are both explicitly model...

Keyword Spotting is an alternative method for retrieving user specified text or query word images, without Optical Character Recognition (OCR), by calculating the similarity between features of word images rather than ASCII content. However, because of unconstrained writing styles with large variations, the retrieving results are always not very satisfactory. In this paper, we propose a novel m...

The output of handwritten word recognizers tends to be very noisy due to factors such as variable handwriting styles, distortions in the image data, etc. In order to compensate for this behaviour, several choices of the word recognizer are initially considered but eventually reduced to a single choice based on constraints posed by the particular domain. In the case of handwritten sentence/phras...

The research activity related to Optical Character Recognition (OCR) for almost all Indian languages is very less. Gujarati script is one of the scripts for which very less literature is available, as far as OCR activities are concerned. This paper describes one of the important phase of OCR, segmentation of handwritten words into its basic components namely basic characters, conjunct character...

This paper discusses a probabilistic on-line handwriting recognition scheme, based on Hidden Markov Models (HMM's), and its implementation for recognizing handwritten words captured from a tablet. Statistical methods, such as HMM's have been used successfully for speech recognition. These methods have recently been applied to the problem of handwriting recognition as well. This paper, discusses...

Abstract—In the context of the handwriting recognition, we propose an off line system for the recognition of the Arabic handwritten words of the Algerian departments. The study is based mainly on the evaluation of neural network performances, trained with the gradient back propagation algorithm. The used parameters to form the input vector of the neural network are extracted on the binary image...

We consider the problem of recognizing handwritten mathematical content in classroom videos that capture the content written on the whiteboard and the content spoken by the instructor. While the problem of recognizing handwritten textual content from videos has been studied before, recognition of handwritten mathematical content and the use of audio content from classroom videos to assist in re...

This work shows Information Retrieval experiments performed over handwritten documents produced by a single writer. The same retrieval task has been performed over both manual (no errors) and automatic (Word Error Rate around 45%) transcriptions of 200 handwritten texts. The results show that the performance loss due to recognition errors is acceptable and that Information Retrieval technologie...