Method For Handwriting Identification Research Articles

Handwriting identification using deep convolutional neural network method

Handwriting is a unique thing that produced differently for each person. Handwriting has a characteristic that remain the same with single writer, so a handwriting can be used as a variable in biometric systems. Each person have a different form of handwriting style but with a small possibility that same characters have something commons. This paper proposes a handwriting identification method using sentence segmented handwriting forms. Sentence form is used to get more complete handwriting characteristics than using a single characters or words. Dataset used is divided into three categories of images, binary, grayscale, and inverted binary. All datasets have same image with different in color and consist of 100 class. Transfer learning used in this paper are pre-trained model VGG19. Training was conducted in 100 epochs. Highest result is grayscale images with genuince acceptance rate of 92.3% and equal error rate of 7.7%.

Chinese Handwriting Identification Method Based on Keyword Extraction

Text-independent handwriting identification methods require that features such as texture are extracted from lengthy document image; while text-dependent handwriting identification methods require that the contents of the documents being compared are identical. In order to overcome these confinements, this paper presents a novel Chinese handwriting identification technique. First, Chinese characters are segmented from handwriting document, then keywords are extracted based on matching and voting of local features of character. Then the same-content keywords are used to build training sets, and these training sets of two documents are compared. Because the keywords are similar to signature, the handwriting identification problem is transformed into signature verification problem. Experiments on HIT-MW, HIT-SW and CASIA show this method outperforms many text-independent handwriting identification methods.

A New Text-Independent Handwriting Identification Method

For the disadvantage of traditional direction index histogram (DIH) handwriting identification method, an improved text-independent handwriting identification algorithm is presented. The handwriting image which is prepared to test needs pre-processing, then the normalized image can be obtained. Based on the features distance two factors can be extracted: the writing influence factor and the character influence factor. Compared the features of the handwriting image which is prepared to test with the features of the sample handwriting image, The better handwriting identification accuracy rate can be obtained, which provides problem-solving method for text-independent handwriting identification.

A Persian writer identification method using swarm-based feature selection approach

Handwriting is one of the most famous biometrics which is processed based on image processing and pattern recognition techniques. However, there are a lot of reports that have already been published on handwritten text identification methods and researchers try to improve the accuracy and speed of such methods. This paper presents an offline Persian handwriting identification method in which some new text features are extracted and best ones are selected using a swarm-based approach. The essence of this feature selection method is bees algorithm, which is a modern swarm-based meta-heuristic approach. In the proposed technique, the adaptive neuro-fuzzy inference system ANFIS is employed as classifier and trained by the input feature vectors. It is also compared with a multi-layer perceptron MLP and fuzzy K-nearest neighbour classifiers. To test the proposed method, we have collected a handwritten Persian text dataset from 125 people who have written six sheets with five lines in each of optional Persian texts. Experimental results showed that the prediction accuracy was about 98% in average while the method training time is less than most related works. It seems this method can be extended for other languages by adjusting its parameters.

An Improved Direction Index Histogram Handwriting Identification Method

Based on direction index histogram (DIH) handwriting identification method, a new improved DIH handwriting identification algorithm is proposed. Firstly handwriting image which is prepared to test is pre-treat, then binary and removing noise the normalized image can be obtain. The obtained features of distance are divided into two factors: writing influence factor and character influence factor, then compared the pre-test images with samples of handwriting images according to the features of the images. Experimental results show that the handwriting identification algorithm proposed by this paper has better recognition rate than DIH algorithm, and obtain better result.