Context: The main purpose of this thesis is to build an automatic handwritten digit recognition method for the recognition of connected handwritten digit strings. To accomplish the recognition task, first, the digits were segmented into individual digits. Then, a digit recognition module is employed to classify each segmented digit completing the handwritten digit string recognition task. In this study, different machine learning methods, which are SVM, ANN and CNN architectures are used to achieve high performance on the digit string recognition problem. In these methods, images of digit strings are trained with the SVM, ANN and CNN model with HOG feature vectors and Deep learning methods structure by sliding a fixed size window through the images labeling each sub-image as a part of a digit or not. After the completion of the segmentation, to achieve the complete recognition of handwritten digits.Objective: The main purpose of this thesis is to find out the recognition performance of the methods. In order to analyze the performance of the methods, data is needed to be used for training using machine learning methods. Then digit data is tested on the desired machine learning technique. In this thesis, the following methods are performed: Implementation of HOG Feature extraction method with SVM Implementation of HOG Feature extraction method with ANN Implementation of Deep Learning methods with CNN Methods: This research will be carried out using two methods. The first research method is the ¨Literature Review¨ and the second ¨Experiment¨. Initially, a literature review is conducted to get a clear knowledge on the algorithms and techniques which will be used to answer the first research question i.e., to know which type of data is required for the machine learning methods and the data analysis is performed. Later on, with the knowledge of RQ1, Experimentation is conducted to answer the RQ2, RQ3, RQ4. Quantitative data is used to perform the experimentation because qualitative data which obtains from case-study and survey cannot be used for this experiment method as it contains non-numerical data. In this research, an experiment is conducted to find the best suitable machine learning method from the existing methods. As mentioned above in the objectives, an experiment is conducted using SVM, ANN, and CNN. By considering the results obtained from the experiment, a comparison is made on the metrics considered which results in CNN as the best method suitable for Documents Images. Results: Compare the results for SVM, ANN with HOG Feature extraction and the CNN method by using segmented results. Based on the Experiment results it is found that SVM and ANN have some drawbacks like low accuracy and low performance in the recognition of documented images. So, the other method i.e., CNN has greater performance with high accuracy. The following are the results of the recognition rates of each method. SVM performance - 39% ANN performance - 37% CNN performance - 71%. Conclusion: This research concentrates on providing an efficient method for recognition of automatic handwritten digits recognition. Here a sample training data is treated with existing machine learning and deep learning methods like SVM, ANN, and CNN. By the results obtained from the experimentation, it clearly is shown that the CNN method is much efficient with 71% performance when compared to ANN and SVM methods. Keywords: Handwritten Digit Recognition, Handwritten Digit Segmentation, Handwritten Digit Classification, Machine Learning Methods, Deep Learning, Image processing on document images, Support Vector Machine, Conventional Neural Networks, Artificial Neural Networks

Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2016-03-15T14:22:48Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Tese - Alberto Lopes_FINAL.pdf: 3638051 bytes, checksum: eaabca9285409b7fd175305c73677557 (MD5) / Made available in DSpace on 2016-03-15T14:22:48Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Tese - Alberto Lopes_FINAL.pdf: 3638051 bytes, checksum: eaabca9285409b7fd175305c73677557 (MD5) Previous issue date: 2015-02-26 / Dentre os problemas e desafios que permeiam o processo de digitalização de documentos e todos os passos subsequentes até a transposição da informação para o meio digital, dois pontos específicos são focados: o texto partido ou degradado e texto escrito em tamanha proximidade que geram sobreposições dos traços. Assim, métodos para solucionar tais problemas, foram pesquisados e desenvolvidos. Baseamos nossa abordagem na emulação de forças físicas de inércia e centrípeta pois entendemos que estas podem ser bem utilizadas para o processamento de imagens de caracteres manuscritos. Para o problema de dígitos partidos, foi desenvolvida uma solução para a restauração de dígitos isolados quebrados e de cadeias de dígitos quebrados através da emulação das forças centrípeta e de inércia. Esta solução tem como princípio gerar uma reconstrução da quebra de modo que se assemelhe à escrita do dígito em questão. Também é abordado a sobreposição de pares de dígitos, problema para o qual foi proposta uma solução de segmentação. Esta solução de segmentação se baseia no conceito de uma bola deformável que tem seus movimentos regidos pela emulação da força de inércia e pela deformação que lhe é permitida receber. Ainda, para desenvolvimento e experimentação dos métodos, foram formadas bases de imagens pertinentes a cada aplicação. Os resultados obtidos mostram desempenhos promissores. Ao aplicar a reconstrução, obtivemos um ganho de aproximadamente seis pontos percentuais em taxa de reconhecimento em relação ao reconhecimento dos dígitos partidos. Já a segmentação provou que supera outros dois métodos de segmentação quando aplicamos o reconhecimento aos dígitos segmentados. Também deve-se ressaltar a questão do custo computacional, especificamente a solução voltada para a segmentação de dígitos sobrepostos, onde seu custo se apresenta mais baixo em relação aos métodos similares pesquisados e testados. Assim, mostramos que os métodos propostos atingem seus objetivos, aliando bons desempenhos com custos computacionais baixos. / Among the problems and challenges that surround the process of document digitization and all subsequent steps until the conversion of the information to a digital medium, two specific steps are focused: broken text and text written in such proximity that cause overlapping of strokes. Methods to solve these problems were researched and developed. We base our approach on the emulation of physical forces of inertia and centripetal force, since it is our understanding that the emulation of such forces can be used for the processing of images of handwritten characters and digits. For the problem of broken digits, a solution for the restoration of isolated broken digits and chains of broken digits through the emulations of inertia and centripetal force was developed. This solution has as principle to generate a reconstruction of the break in such a way that it resembles closely the writing style of the digit in question. We also tackle overlapping pairs of digits, problem for which we propose a segmentation solution. This segmentation is based on the concept of a deformable ball that has its movements governed by the emulation of inertia and the degree of deformation the ball is allowed to have. For development and experimentation of the created methods, image databases pertinent to each application were formed. The obtained results show promising performance. When applying the reconstruction, we obtained a gain of approximately six percentage points in recognition rates when compared to rates obtained for broken digits. In regards to segmentation, it proved to outperform two other methods when recognition is applied to the output segmented digits. The computational cost of the methods should also be pointed out, specifically regarding the solution created for the segmentation of overlapped digits, which is lower when compared to other similar methods that were researched and tested. Therefore, we show that the proposed methods reach their goals, coupling performance with low computational costs.