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Chinese and English text recognition based on deep learning
1 Introduction
1.1 Research background and significance
Words are one of the most common ways for human beings to communicate information. In the digital age, it is very important to convert printed or handwritten words into a processable electronic form. Conventional rule-based methods perform poorly on complex text recognition tasks because the shapes and appearances of text vary greatly. Traditional text recognition refers to the text recognition technology based on traditional computer vision and pattern recognition methods used before the emergence of deep learning. The process of traditional text recognition is shown in the figure.
In traditional text recognition methods, researchers mainly focus on how to extract effective features from images to represent text. These features can include shapes, textures, edges, shadows, etc. Traditional feature extraction methods such as SIFT, HOG and SURF are widely used in text recognition. Second, character recognition employs an appropriate feature classifier to match the extracted features with known word categories. Traditional classifiers include support vector machines (SVM), nearest neighbors (KNN), and random forests. Traditional text recognition research usually requires a large number of labeled samples to train and evaluate models. Researchers construct and maintain various text datasets, which contain text images of different fonts, sizes, orientations, and qualities. To annotate these datasets, human involvement is usually required, which is a time-consuming and laborious process. Although the traditional text recognition method has achieved success to a certain extent, due to its limitations, with the emergence of deep learning, the research focus has gradually shifted to OCR technology based on deep learning. The introduction of deep learning has greatly improved the performance of text recognition, making the OCR system more accurate, robust and flexible. The process of deep learning text recognition is shown in the figure. 
With the rapid development of deep learning technology, especially the emergence of convolutional neural network (CNN) and recurrent neural network (RNN), methods based on deep learning have achieved great success in text recognition tasks. Remarkable progress Deep learning-based text recognition is an important research area with wide-ranging applications in computer vision and natural language processing. Text recognition aims to convert printed or handwritten text into an editable and searchable digital form.
Text recognition has a wide range of applications in real life, such as automated processing of scanned documents, automated bank check processing, document archiving in digital libraries and archives, etc. Deep learning-based methods can also be combined with other computer vision and natural language processing tasks, such as text translation and semantic understanding, to achieve more complex application scenarios. In the past, traditional OCR methods were usually based on hand-designed feature extraction and classifiers, whose performance was limited. However, deep learning models have the ability to automatically learn feature representations, which can extract high-level features from raw image data, thereby improving the accuracy and robustness of OCR systems. Deep learning has made remarkable progress in the field of optical character recognition (OCR). OCR is a technology that converts printed or handwritten text into machine-readable text. It is widely used in various fields, including document digitization, automated data entry, autonomous driving, smart office, etc. The introduction of deep learning technology enables OCR systems to process various text images more accurately and efficiently, which is of great significance for information processing and automation tasks.
1. Improve accuracy: Traditional OCR methods often do not perform well when dealing with complex scenes, low-quality images, or handwritten text. The deep learning model can learn richer and more robust feature representations through large-scale data training, thereby improving the accuracy of the OCR system.
2. Handling multi-language and multi-font: With the development of globalization, handling multi-language and multi-font has become an important requirement of the OCR system. The deep learning model can learn language and font features directly from the data through an end-to-end learning method without manually designing feature extractors, thus easily extending to different languages and fonts.
3. Reduce manual intervention: Traditional OCR methods usually rely on domain experts for feature design and parameter tuning. The deep learning model can automatically learn feature representation and classifier from raw data through end-to-end learning, which reduces the need for manual intervention and improves the automation of the system.
4. Accelerated processing speed: The training and inference of deep learning models can be efficiently implemented on the GPU through parallel computing. By optimizing the network structure and algorithm, the processing speed of the OCR system can be further improved to meet the application requirements of real-time or high throughput.
In conclusion, the research of deep learning OCR provides important technical support for realizing high-accuracy, multi-language, multi-font, low-cost and high-efficiency text recognition system. Its wide application in practice will promote the development of information processing and automation tasks, bringing more convenience and benefits. The research on character recognition based on deep learning has important background and significance. It promotes the development of character recognition technology, improves the accuracy and robustness of character recognition, and provides more possibilities for practical applications.
1.2 Research Status
Text recognition based on deep learning is an important research direction in the fields of computer vision and natural language processing. With the continuous development of deep learning technology, deep learning-based methods have achieved remarkable progress in text recognition tasks.
In the field of text detection and positioning: Zhang et al. (2019) proposed a multi-task text detection and positioning method based on deep learning, which combined target detection and text segmentation technology to achieve efficient and accurate text positioning. Liu et al. (2020) proposed a multi-scale text detection method based on recurrent neural network, which improved the detection performance for small-sized and rotated text by introducing attention mechanism and pyramid feature fusion. The EAST (Efficient and Accurate Scene Text detection) method was proposed by Zhou et al. in 2017. It adopts a fully convolutional network structure and realizes text detection through pixel-level prediction. The method is efficient and accurate, and can handle multi-directional and arbitrary-shaped text. TextBoxes is a text detection method based on convolutional neural network, proposed by Liao et al. in 2016. It uses multi-scale feature maps and multi-directional anchor boxes to detect text regions, which has good robustness and accuracy. The CRAFT (Character Region Awareness for Text detection) method was proposed by Baek et al. in 2019. It adopts a character-based segmentation strategy and can accurately locate the character-level bounding box of text. This method improves the accuracy of text localization by introducing a character-level attention mechanism. FOTS (Fast Oriented Text Spotting with a Unified Network) was proposed by Liu et al. in 2018. It achieves end-to-end text recognition by fusing text detection and positioning tasks. This method uses a rotating rectangular frame to locate text in any direction, and has high speed and accuracy.
In the field of text recognition: He et al. (2019) proposed an end-to-end text recognition method based on convolutional cyclic neural network, and achieved accurate text recognition by jointly training character detection and recognition networks. Bai et al. (2020) proposed an unsupervised text-to-image synthesis method based on deep learning and generative adversarial networks to enhance the robustness of text recognition models. Wang et al. (2020) proposed a multilingual text recognition method based on multi-task learning and attention mechanism, which can simultaneously handle text recognition tasks in multiple languages. Gupta et al. (2021) proposed a multilingual text recognition method based on a multilingual attention generation network, which improves the accuracy and generalization performance of multilingual text recognition by learning character-level alignment relations. In addition, for character data enhancement and model optimization: Chen et al. (2019) proposed a text recognition method based on data enhancement and adaptive learning rate. and adaptability to low-resolution images. Zhang et al. (2021) proposed a text recognition method based on self-supervised learning, which improved the robustness and generalization ability of the text recognition model through unsupervised pre-training and self-generation tasks.
Current academic researchers have proposed various innovative methods and technologies, including text detection and positioning, text recognition, and data enhancement and model optimization. These studies have not only achieved breakthroughs in accuracy and robustness, but also promoted the practical application and development of text recognition technology. Future research can further explore the combination of deep learning and other technologies to improve the performance and application range of text recognition.
2 Feasibility Analysis of Deep Learning Text Recognition
2.1 Overview
The feasibility analysis of deep learning text recognition is based on the following aspects:
Data availability: deep learning methods usually require a large amount of labeled data for training. In the field of text recognition, you can use public text datasets, such as ICDAR, etc., or build and label datasets yourself. The feasibility of deep learning text recognition will be supported if enough high-quality data is available.
Algorithm model and technology development: Deep learning has made remarkable progress in the field of text recognition, and many successful algorithm models and technologies have emerged, such as CRNN, Transformer, etc. These models and technologies have achieved excellent performance in various text recognition tasks, verifying the feasibility of deep learning text recognition.
Computing resources and technical support: Deep learning models typically require massive computing resources for training and inference. With the development of hardware and software technologies, such as GPU acceleration, cloud computing, etc., the availability of computing resources continues to increase, providing sufficient support for deep learning text recognition.
Application scenario requirements: Text recognition has a wide range of application requirements in various fields, such as automated office, image retrieval, license plate recognition, etc. Deep learning methods can provide higher accuracy and robustness through large-scale training and end-to-end learning to meet the needs of practical application scenarios.
However, deep learning text recognition still faces some challenges and limitations:
First, high-quality labeled data is crucial to the effect of deep learning, but data quality and labeling costs may become limiting factors. Especially for texts in certain domains or languages, data collection and annotation may be more difficult and expensive. Second, robustness in complex scenes: deep learning text recognition in complex scenes, such as low light, blurred or occluded images, is still challenging. The robustness and generalization ability of the model need to be further improved. However, deep learning models are often viewed as black boxes, lacking explanation and interpretability for recognition decisions. In some application scenarios, the need to explain the decision-making process may limit the feasibility of deep learning text recognition.
Overall, deep learning text recognition works in most cases with high accuracy and performance. With the continuous development of technology and the abundance of data resources, deep learning character recognition will continue to be widely used in various fields.
2.2 Commonly used deep learning text positioning methods
At present, the deep learning text area positioning methods commonly used in industry and academic use are shown in the figure below, which mainly includes: (
SSDSingle Shot MultiBox Detector) is a single-stage target detection method that can achieve fast and accurate text positioning. It achieves the text localization task by applying multiple predefined anchor boxes on feature maps of different scales to simultaneously predict the location and category of text.
EAST(Efficient and Accurate Scene Text detection) is a deep learning method for scene text detection. It uses a fully convolutional network structure and introduces a rotating rectangular frame to locate text in any direction, which is efficient and accurate.
TextBoxesIt is a text localization method based on convolutional neural network. It uses multi-scale feature maps and multi-directional anchor boxes to detect text areas, and uses regression networks to precisely locate text bounding boxes, which has good robustness and accuracy.
CRAFT(Character Region Awareness for Text detection) is a text positioning method based on character-level segmentation. It utilizes a character-level attention mechanism to improve the accuracy of text localization by segmenting text regions into character-level bounding boxes.
FOTS(Fast Oriented Text Spotting with a Unified Network) is an end-to-end text positioning and recognition method. By fusing text detection and recognition tasks, it uses a rotating rectangular box to locate text in any direction, and realizes end-to-end text recognition.
2.3 Commonly used deep learning text recognition methods
At present, the deep learning text area positioning methods commonly used in industry and academic use are shown in Figure 2-2 below. Commonly used deep learning text recognition methods include: (Convolutional
CRNNRecurrent Neural Network) A classic deep learning text recognition method. It combines convolution and RNN to realize direct recognition of variable-length text.
TransformerIt is a deep learning model based on self-attention mechanism, originally used for machine translation tasks. In the field of text recognition, the Transformer model has also been successfully applied. It mainly establishes the global context relationship and models the text sequence, which has good recognition accuracy and generalization ability.
TesseractIt is an open source text recognition engine based on deep learning and traditional pattern recognition methods. It has high recognition accuracy, multilingual support, and can be extended by training on custom data.
STAR-Net(Spatial Temporal Attention ResNet) is a deep learning method for multilingual text recognition. It combines spatial and temporal attention mechanisms, can handle text recognition tasks in multiple languages, and has good robustness and accuracy.
CALAMARIIt is an open source multilingual text recognition framework based on technologies such as deep learning and connection temporal classification (CTC). It combines CNN, RNN and CTC to achieve high-performance text recognition across languages.
FOTS(Fast Oriented Text Spotting) is an end-to-end text recognition method that can achieve fast and accurate text detection and recognition. It utilizes rotating rectangles to locate text in arbitrary orientations by fusing text detection and recognition tasks.
Summarize
Text recognition based on deep learning has made remarkable progress in recent years, which mainly consists of two key steps: text positioning and text recognition. Currently, text positioning uses the DB (Differentiable Binarization) algorithm, while text recognition uses the CRNN (Convolutional Recurrent Neural Network) algorithm. This review will summarize the application of these methods in the field of text recognition and introduce the rationale for implementing them using the PaddlePaddle framework.
Text positioning is the pre-step of text recognition, and its goal is to accurately locate the text area in the image. The DB algorithm solves the problem of fixed threshold in the traditional binarization method by means of adaptive threshold. It utilizes a deep learning network to predict whether each pixel belongs to a text region, generating a binary segmentation mask. The method has good robustness and adaptability, and can accurately locate text according to the brightness and contrast changes of different images. Text recognition is the process of converting images of words into understandable text. The CRNN algorithm combines the advantages of convolution and RNN, and can simultaneously process the spatial features and sequence information of images.