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.

Fuzzy Full-Text Searches in OCR Databases

Abstract: Though the quality of optical character recognition software is steadily improving, it is still far from being perfect. As a result, full-text databases that are lled by means of OCR software contain many errors. These errors have to be taken into consideration if such kind of databases are examined by means of full-text searches. In this chapter, we will illustrate some of the possible methods that { to a certain extent { cope with the uncertainty of the database entries. These methods add fuzziness to precisely formulated queries in order to increase their recall. In addition, the described methods are compared to the method of matching query terms exactly: the preliminary results of tests that show their eeects on recall and precision are given.

Processes In Human Kanji-word Recognition

Abstract: This paper proposes a human Kanji-word recognition model consisting of two separate processing stages: an early parallel stage and the later serial stage. The model is influenced by word shape knowledge only at the early parallel stage. The model interprets several experimental results- forced-choice identification is more accurate for Kanji characters in Kanji-words than for single Kanji characters in dissimilar alternative sessions, but less accurate in similar alternative sessions. An application of this model to the development of a Kanji-word recognition machine is discussed.

Affixal approach for Arabic decomposable vocabulary recognition a validation on printed word in only one font

Abstract: We propose a new approach for Arabic word recognition called affixal approach. This approach is founded on morphological structure of Arabic vocabulary. A mechanism of decomposition-recognition is used in our approach and makes it possible to lead to a set of reliable solutions for each word. This mechanism tries to recognize word basic morphemes: prefix, infix, suffix and root contrary to existing approaches which are usually based on recognition of word entity by holistic approach, pseudo-word entity by pseudo-analytical approach or letter entity by analytical approach. In this paper, we will present limits of existing approaches for Arabic word recognition. We will expose then Arabic vocabulary structure. We will detail after affixal approach for Arabic decomposable vocabulary recognition with a word example. Lastly, we will expose experimental results obtained on a basis of 1000 words data set.

Single-character type identification

Abstract: Different character recognition problems have their own specific characteristics. The state-of-art OCR technologies take different recognition approaches, which are most effective, to recognize different types of characters. How to identify character type automatically, then use specific recognition engines, has not brought enough attention among researchers. Most of the limited researches are based on the whole document image, a block of text or a text line. This paper addresses the problem of character type identification independent of its content, including handwritten/printed Chinese character identification, and printed Chinese/English character identification, based on only one character. Exploiting some effective features, such as run-lengths histogram features and stroke density histogram features, we have got very promising result. The identification correct rate is higher than 98% in our experiments.

Handwritten Character Recognition using Different Kernel based SVM Classifier and MLP Neural Network (A COMPARISON)

Abstract: Neural Networks and SVM are recently being used in various kind of pattern recognition. As humans, it is easy to recognize numbers, letters, voices, and objects, to name a few. However, making a machine solve these types of problems is a very difficult task .Character Recognition has been an active area of research in the field of image processing and pattern recognition and due to its diverse applicable environment, it continues to be a challenging research topic. It has numerous applications which include, reading aid for blind, bank cheques and conversion of any hand written document into structural text form. In this paper an attempt is made to recognized handwritten character using the multi layer feed forward back propagation neural network without feature extraction and SVM classifier. Character data is used for training the neural network and SVM. The trained network is used for classification and recognition. For the neural network, each character is resized into 70x50 pixels, which is directly subjected to training. That is, each resized character has 3500 pixels and these pixels are taken as features for training the neural network. For the SVM classifier recognition model is divided in two phases namely, training and testing phase. In the training phase 25 features are extracted from each character and these features are used to train the SVM. In the testing phase SVM classifier is used to recognize the characters. The results show that by applying the proposed system, we reached a high accuracy for the problem of handwritten character recognition.