本文選題：聯(lián)機手寫體識別 + 手寫體數(shù)學(xué)公式��； 參考：《電子科技大學(xué)》2017年碩士論文

【摘要】：在教育行業(yè),為了實時追蹤學(xué)生的學(xué)習(xí)軌跡和知識薄弱環(huán)節(jié),機器自動識別學(xué)生答題的手寫筆跡成為必要的技術(shù)需求。因此,本文的研究重點為聯(lián)機數(shù)學(xué)公式手寫體識別,旨在提出一個穩(wěn)健可行的解決方案來識別學(xué)生的手寫數(shù)學(xué)公式筆跡,主要研究內(nèi)容包括如下幾點:1、提出了一種融合CNN和DBN的單字符識別模型應(yīng)用卷積神經(jīng)網(wǎng)絡(luò)(Convolutional Neural Network,CNN)搭建和訓(xùn)練了一個單字符分類器模型,針對神經(jīng)網(wǎng)絡(luò)對對抗樣本的脆弱性表現(xiàn),同時創(chuàng)造性的采用了深度信念網(wǎng)絡(luò)(DeepBeliefNetwork, DBN)的解碼重構(gòu)損失作為識別置信度評價模型,最后融合了 CNN和DBN的置信度評價,增強了對對抗樣本的拒識能力。2、提出了一種基于組合排序的手寫體識別算法手寫數(shù)學(xué)公式中存在著大量的二義性,二維結(jié)構(gòu)準(zhǔn)確判定存在著相當(dāng)?shù)碾y度,同時還有許多容易混淆的相似字符,這些情況都增大了機器自動識別的難度。本文提出了一種基于組合排序的手寫體識別算法,先把這些不確定的情況都保存下來作為候選,產(chǎn)生候選組合路徑,再基于詞組頻率表,語義模型,識別置信度等進行路徑排序就能保證識別正確率,可以大大簡化系統(tǒng)的復(fù)雜度,同時增大識別系統(tǒng)的魯棒性。3、提出了一種基于錯誤識別案例的快速學(xué)習(xí)方法識別出錯案例的調(diào)試工作非常繁重,本文提出了一個基于錯誤案例的學(xué)習(xí)方法,可以快速的從我們提供的錯誤案例標(biāo)記數(shù)據(jù)中學(xué)習(xí)到新的組合映射知識,并將這些知識作為系統(tǒng)的組合補充分支,避免了再次出現(xiàn)同樣的識別錯誤�；谏鲜龅姆椒�,實現(xiàn)了一個識別系統(tǒng),實驗結(jié)果表明本文提出的聯(lián)機數(shù)學(xué)公式手寫體識別方法具有較高的識別率及較好的魯棒性。
[Abstract]:In the education industry, in order to track the students' learning track and knowledge weakness in real time, it is necessary to recognize the handwritten handwriting of students' answer questions automatically by machine. Therefore, this paper focuses on on-line mathematical formula handwritten recognition, in order to propose a robust and feasible solution to recognize the handwriting of students' handwritten mathematical formula. The main research contents are as follows: 1. A single character recognition model combining CNN and DBN is proposed. A single character classifier model is built and trained by convolution neural network Convolutional Neural Network. At the same time, it creatively adopts the decoding reconstruction loss of deep belief network (DBN) as the evaluation model of recognition confidence. Finally, it combines the confidence evaluation of CNN and DBN. In this paper, we enhance the ability of rejecting the antagonistic samples. 2. A handwritten recognition algorithm based on combinatorial sorting is proposed. There is a lot of ambiguity in the handwritten mathematical formula, and it is very difficult to determine the 2D structure accurately. At the same time, there are many confusing similar characters, which make automatic recognition more difficult. In this paper, a handwritten recognition algorithm based on combinatorial sorting is proposed. Firstly, these uncertainties are saved as candidates to produce candidate combination paths, and then based on phrase frequency table, semantic model. The correct rate of recognition can be guaranteed by the path sorting of recognition confidence, and the complexity of the system can be greatly simplified. At the same time, the robustness of the recognition system is increased. A fast learning method based on the error recognition case is proposed. The debugging work of identifying the error case is very heavy. In this paper, a learning method based on the error case is proposed. We can quickly learn new combinatorial mapping knowledge from the error case tag data provided by us and use this knowledge as a complementary branch of the system to avoid the same recognition errors. Based on the above method, a recognition system is implemented. The experimental results show that the on-line mathematical formula handwritten recognition method proposed in this paper has a higher recognition rate and better robustness.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41;TP18

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本文編號：1883936