Comparative Study on Deep Convolution Neural Networks DCNN-Based Offline Arabic Handwriting Recognition

Abstract

Recently, deep learning techniques demonstrated efficiency in building better performing machine learning models which are required in the field of offline Arabic handwriting recognition. Our ancient civilizations presented valuable handwritten manuscripts that need to be documented digitally. If we compared between Latin and the isolated Arabic character recognition, the latter is much more challenging due to the similarity between characters, and the variability of the writing styles. This paper proposes a multistage cascading system to serve the field of offline Arabic handwriting recognition. The approach starts with applying the Hierarchical Agglomerative Clustering (HAC) technique to split the database into partially interrelated clusters. The interrelations between the constructed clusters support representing the database as a big search tree model and help to attain a reduced complexity in matching each test image with a cluster. Cluster members are then ranked based on our new proposed ranking algorithm. This ranking algorithm starts with computing Pyramid Histogram of Oriented Gradients (PHoG), and is followed by measuring divergence by Kullback-Leibler method. Eventually, the classification process is applied only to the highly ranked matching classes. A comparative study is made to assess the effect of six different deep Convolution Neural Networks (DCNNs) on the final recognition rates of the proposed system. Experiments are done using the IFN/ENIT Arabic database. The proposed clustering and ranking stages lead to using only 11% of the whole database in classifying test images. Accordingly, more reduced computation complexity and more enhanced classification results are achieved compared to recent existing systems. INDEX TERMS Agglomerative hierarchical clustering, deep convolutional neural network, Kullback-Leibler divergence, offline arabic handwritting recognition, pyramid histogram of gradients.

[Retracted] Prediction and Early Warning of Regional Coordinated Development Based on Convolution Neural Network Algorithm

“…China's multiregional economy and society are still in the status of unbalanced development, energy production cannot meet the needs of industrial structure and regional development, and the consequence of rapid economic development is that the environment has been seriously damaged. In the long run, the coordinated development of the region will be seriously affected, so in view of this situation, we need three factors in the region to restrict each other, thus forming a unified whole of energy, economy, and environment, which is called 3E system for short [20][21][22].…”

Recognition of Off-line Handwritten Uyghur Words Using Bayesian Networks with Grapheme Nodes

“…Currently, cursive word recognition algorithms are commonly based on the holistic word recognition strategy. Studies have been carried out on the recognition of handwritten Arabic words using a [10]. In addition, algorithms based on multi-stream hidden Markov model (HMM) [7], support vector machine (SVM) classifier [8], and multi-classifier fusion [9] have also achieved good results in off-line handwritten Arabic word recognition.…”

“…Currently, the two dominant strategies for recognizing cursive words can be divided according to whether character segmentation is conducted [6], i.e., holistic word recognition [7][8][9][10] and segmentation-driven recognition [11][12][13]. Algorithms that conduct training and recognition of words as a whole are relatively simple, but their ability to discriminate small differences between similar words is comparatively poor.…”

Deep learning-based isolated handwritten Sindhi character recognition

“…This is mainly due to the language complexities, complex document layout and different unique characteristics associated to the Sindhi language. Handwritten Sindhi character recognition is more challenging than the printed Sindhi character recognition due to: (1) handwritten Sindhi characters have more variations in terms of aspect ratio when written by different writers or the same writer, (2) handwritten Sindhi text has no defined patterns and depends upon the quality of the writer's writing, (3) different shapes of the same character such as isolated, initial, medial and final make the recognition problem further complex (4) ligature overlapping makes the segmentation of characters more difficult (5) several characters have similar basic shape but they differ either by the number of dots or their positions around the shape, (6) it is cursive in nature and is written in the right to left direction (7) interconnections of two or more characters and several other challenges further reduce the recognition accuracy of handwritten Sindhi characters. Figure 1 shows different shapes of the same character used within a word, while Figure 2 shows the two groups of Sindhi characters with same baseline shape but different number of dots, orientations or their positions around the shape.…”

“…In recent years, deep learning networks particularly CNNs have become most common used methods to solve image processing, pattern recognition and several other computer vision problems. These networks have demonstrated state-of-the-art performance for the Arabic and Urdu handwritten character recognition (4)(5)(6) than other methods. Further, CNNs are capable to classify and recognize text at word or character levels without prior information about the structure of the language.…”