COMPARISON OF IMAGE ANALYSIS FOR THAI HANDWRITTEN CHARACTER RECOGNITION Olarik Surinta, chatklaw Jareanpon Department of Management Information System Faculty of Informatics Mahasarakham University

Home Introduction Data Pre- Processing Method Training Method Conclusion Experimental Result ICIIP 2006 INTRODUCTION This article presents a comparison of image analysis for Thai handwritten character recognition. There are three steps of our methodology, –Data Pre-processing Method –Training Method –Pattern Recognition Method

Home Introduction Data Pre- Processing Method Training Method Conclusion Experimental Result ICIIP 2006 INTRODUCTION Data Preprocessing Method –Data preprocessing method is based on feature extraction to find the edge of character-image (described by a group of Fourier Descriptors: FD).

Home Introduction Data Pre- Processing Method Training Method Conclusion Experimental Result ICIIP 2006 INTRODUCTION Training Method –Training method is based on Back-Propagation Neural Network and Robust C- Prototype technique to processed Thai Handwritten character images to identify the centroid of each character, resulting in 44 prototypes.

Home Introduction Data Pre- Processing Method Training Method Conclusion Experimental Result ICIIP 2006 INTRODUCTION There are 44 Thai characters: ก ข ฃ ค ฅ ฆ งจ ฉ ช ซ ฌ ญ ฎ ฏ ฐ ฑ ฒ ณด ต ถ ท ธ น บ ป ผ ฝ พ ฟภ ม ย ร ล ว ศ ษ ส ห ฬ อ ฮ

Home Introduction Data Pre- Processing Method Training Method Conclusion Experimental Result ICIIP 2006 INTRODUCTION Pattern Recognition Method –Pattern recognition method is based on Back-Propagation Neural Network and Robust C- Prototype technique to identify the unknown image (Thai handwritten character).

Home Introduction Data Pre- Processing Method Training Method Conclusion Experimental Result ICIIP 2006 DATA PREPROCESSING METHOD Character-images are images of Thai handwritten characters. The output will be stored in the term of digital data by scanning. One bitmap file with gray scale pattern and 256 levels specifics one character. Figure 1 A prototype character-image.

Home Introduction Data Pre- Processing Method Training Method Conclusion Experimental Result ICIIP 2006 Binarization Binarization converts gray-level image to black-white image, and to extracting the object component from background, this scheme will check on every point of pixel. Figure 2 The example of binarization scheme.

Home Introduction Data Pre- Processing Method Training Method Conclusion Experimental Result ICIIP 2006 Binarization (A) (B) (C) Figure 3 The diagram of extracting the object from the background component in the image.

Home Introduction Data Pre- Processing Method Training Method Conclusion Experimental Result ICIIP 2006 Edge Detection Edge detection is one of an important image processing phases. This paper uses chain code technique to detect the image’s edge. The direction has been classified by 8 categories: Figure 4 Chain code with 8 directions.

Home Introduction Data Pre- Processing Method Training Method Conclusion Experimental Result ICIIP 2006 Edge Detection Once the edge of image has discovered, shown in figure 3, the process needs to find the character line. The coordinate is represented by complex number as the formula: Figure 5 coordinate represented in character image.

Home Introduction Data Pre- Processing Method Training Method Conclusion Experimental Result ICIIP 2006 Fourier Descriptors Fourier Features used to describe the edge of the object works by identify coordinate ; K = 0, 1, …, N-1 where N is any other area in the image. All point will be represents as complex number. Therefore, the DFT can be derived as below:

Home Introduction Data Pre- Processing Method Training Method Conclusion Experimental Result ICIIP 2006 Fourier Descriptors From the above formula, coefficient vector will be automatically calculated. This vector fits as 1 dimension with the size of 1x10 or 1xn Figure 6 Fourier Descriptors of Image.

Home Introduction Data Pre- Processing Method Training Method Conclusion Experimental Result ICIIP 2006 TRAINING METHOD Training method is based on –Back-Propagation Neural Network and –Robust C-Prototype technique

Home Introduction Data Pre- Processing Method Training Method Conclusion Experimental Result ICIIP 2006 Robust C-Prototype RCP can be determined in grouping phase in order to estimate C-Prototypes spontaneously, utilizing loss function and square distance to reduce some noise. The definition can be expressed as:

Home Introduction Data Pre- Processing Method Training Method Conclusion Experimental Result ICIIP 2006 Robust C-Prototype The diagram of solving the problem by RCP is shown in figure 7 Figure 7 RCP algorithm.

Home Introduction Data Pre- Processing Method Training Method Conclusion Experimental Result ICIIP 2006 Back-Propagation Neural Network Back propagation neural network use a learning algorithm use in which an error moves from the output layer to the input one. Figure 8 The Neural Network Layer.

Home Introduction Data Pre- Processing Method Training Method Conclusion Experimental Result ICIIP 2006 Back-Propagation Neural Network Back-Propagation neural networks consists of several neuron layers, each neuron of layer being connected to each neuron of layer. In the training phase, the correct class for each record is known (this is termed supervised training), and the output nodes can therefore be assigned "correct" values -- "1" for the node corresponding to the correct class, and "0" for the others.

Home Introduction Data Pre- Processing Method Training Method Conclusion Experimental Result ICIIP 2006 Back-Propagation Neural Network These error terms are then used to adjust the weights in the hidden layers so that, hopefully, the next time around the output values will be closer to the "correct" values. The network processes the records in the training data one at a time, using the weights and functions in the hidden layers, and then compares the resulting outputs against the desired outputs.

Home Introduction Data Pre- Processing Method Training Method Conclusion Experimental Result ICIIP 2006 Back-Propagation Neural Network Errors are then propagated back through the system, causing the system to adjust the weights for application to the next record to be processed. During the training of a network the same set of data is processed many times as the connection weights are continually refined.

Home Introduction Data Pre- Processing Method Training Method Conclusion Experimental Result ICIIP 2006 EXPERIMENTAL RESULT The data in this experiment have 2 sets. –The first set is “the learning set” containing 4,400 characters. –The second set is the “test set” containing 440 characters. All data is Thai handwritten and generated by 100 persons.

Home Introduction Data Pre- Processing Method Training Method Conclusion Experimental Result ICIIP 2006 EXPERIMENTAL RESULT Topic Robust C- Prototype Back- Propagation neural network Time of learning 1.5 Hour2.45 Hour Accuracy test on “Test set” 91.5%88% Comparison between Robust C-Prototype and Back- propagation neural network

Home Introduction Data Pre- Processing Method Training Method Conclusion Experimental Result ICIIP 2006 CONCLUSION This paper looks at Thai handwritten character recognition. We compared Robust C-Prototype and Back- Propagation neural networks. The experimental results of both methods have accuracy more than 85%.

Home Introduction Data Pre- Processing Method Training Method Conclusion Experimental Result ICIIP 2006 Figure 9 The character images the adjustment scheme.