The most common and challenging issues in image recognition are scene character recognition from the street view image, and the scene character consists of both text and number. Recently, the researchers were introduced a lot of scene character recognition methods, but the performance of the methods often degraded due to complexity. So, we proposed the improved firefly algorithm for local trapping problem (IFLT) utilizing convolutional neural network (CNN) for the extraction of features from the scene character. The IFLT approach is the improved version of the firefly optimization algorithm to solve local trapping problems. During feature extraction, the hyperparameters on CNN are tuned with the help of the IFLT approach. The alignment and multilayer perceptron layers are used on CNN. Subsequently, the support vector machine approach is used to classify the relevant class of scene characters from the street view image. Experimentally, we use six scene character dataset SVHN, ISN, IIIT5K-words, SVT, ICDAR 2003, and ICDAR 2013 dataset. The performance of the proposed IFLT approach is evaluated with standard deviation, mean, average computational time, and most excellent minimum (MEmin) parameters. The experimental results demonstrate that the proposed IFLT-CNN is well suitable for scene character recognition.

图像识别中最常见和最具挑战性的问题是从街景图像中识别场景角色，并且场景角色包括文本和数字。最近，研究人员介绍了许多场景字符识别方法，但是由于复杂性，这些方法的性能经常下降。因此，我们提出了一种改进的萤火虫局部捕获问题算法（IFLT），该算法利用卷积神经网络（CNN）从场景角色中提取特征。IFLT方法是萤火虫优化算法的改进版本，可以解决局部陷阱问题。在特征提取期间，借助IFLT方法对CNN上的超参数进行调整。排列和多层感知器层用于CNN。后来，支持向量机方法用于从街景图像中对场景角色的相关类别进行分类。实验上，我们使用六个场景角色数据集SVHN，ISN，IIIT5K单词，SVT，ICDAR 2003和ICDAR 2013数据集。所提出的IFLT方法的性能通过标准偏差，均值，平均计算时间和最出色的最小值（MEmin）参数进行评估。实验结果表明，提出的IFLT-CNN非常适合场景字符识别。以及最出色的最小（MEmin）参数。实验结果表明，提出的IFLT-CNN非常适合场景字符识别。和最出色的最小（MEmin）参数。实验结果表明，提出的IFLT-CNN非常适合场景字符识别。