OCR is widely applied in scenarios such as general text recognition. OCR systems accelerated by FPGA-based heterogeneous architectures offer significant advantages over traditional CPU or GPU implementations, including lower latency and reduced costs. We have developed a multi-FPGA collaborative architecture that enables rapid adaptation to changes in OCR models. This system achieves 130% of the performance of a GPU P4, with processing delays only one-tenth of P4 and one-thirtieth of a CPU.
Character Recognition Technology - OCR
OCR technology refers to the process of detecting and recognizing text within images. It has become a fundamental tool in various applications, including document scanning, e-books, automatic data entry, and license plate identification. The challenge lies in its ability to recognize text in any scene without requiring customization for specific environments, making it a key area of research in artificial intelligence.
The general OCR pipeline consists of two core components: text detection and text recognition. The detection model identifies regions containing text and outlines them, while the recognition model processes these detected regions to extract the actual characters.
In recent years, deep learning has been increasingly applied to sequence modeling tasks, such as audio, video, and natural language processing. End-to-end learning in deep neural networks has significantly improved the performance of sequence learning. A common approach involves combining Convolutional Neural Networks (CNN) with Recurrent Neural Networks (RNN). CNNs are used to extract spatial features from images, while RNNs introduce temporal context, enhancing the accuracy of text detection. This hybrid CNN+RNN architecture has greatly improved the performance of text recognition tasks.
The CRNN model, which is widely used today, is a combination of DCNN and RNN. It can be trained end-to-end without requiring detailed annotations, and it has fewer parameters than standard CNN models. Additionally, CRNN maintains the sequential relationship between image features and text content, making it particularly effective for recognizing complex or fragmented text sequences.
The CRNN architecture consists of three main parts:
1) The convolutional layer extracts a sequence of features from the input image, preserving spatial order and generating feature vectors.
2) The recurrent layer uses bidirectional LSTM to capture contextual information and predict character categories.
3) The transcription layer employs CTC (Connectionist Temporal Classification) along with forward-backward algorithms to determine the optimal label sequence.
OCR Acceleration Architecture
Given the programmability, high performance, and high bandwidth of FPGAs, we designed a heterogeneous acceleration architecture using multiple FPGA chips. Each chip is customized for a specific type of model, and the overall system leverages load balancing and pipelining across different chips to achieve efficient acceleration.
FPGA 0 is configured as a general CNN accelerator, while FPGA 1 acts as an LSTM accelerator. A CPU handles small-scale fully connected layers to maintain model flexibility. Data communication between FPGAs and the CPU occurs via PCIe Gen3, with the CPU managing load balancing. Inter-FPGA communication uses the AURORA protocol, achieving nanosecond-level latency, similar to shared memory between boards. Future upgrades will support multitasking and scheduling across servers.
By optimizing the underlying architecture for specific deep learning models, we maximize the computational efficiency of the heterogeneous system, ensuring optimal performance and scalability.
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