NextOCR : A Next-Generation Document Text Recognition System

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By Next SolutionLab on 2024-10-22 21:26:28

Introduction

With the rise in digital transformation, the need to extract text from physical and digital documents efficiently has become crucial for businesses across various industries. Traditional Optical Character Recognition (OCR) systems often struggle with complex layouts, skewed images, or partial text detection. To solve these challenges, we developed an advanced text recognition system that uses YOLOv7 for text detection and a TPS-ResNet-BiLSTM-Attention (TRBA) architecture for text recognition. Let's dive into the details of how this system is revolutionizing document processing.

Key Features

1. YOLOv7 for Precision Text Detection

YOLOv7 is one of the most powerful object detection models available today, and I used it to accurately detect text in documents with complex layouts. Whether dealing with multi-column invoices or dense text in books, YOLOv7 achieved an impressive 98.482% accuracy in pinpointing text locations within documents. To know more about YOLOv7, please go through this paper.

2. Text Recognition Pipeline

The TRBA text recognition module was built using a highly effective combination of techniques:

TPS (Thin Plate Spline): Ensures that text within irregularly shaped or distorted regions is accurately transformed. To know more about TPS.

ResNet: A powerful feature extractor that captures essential patterns in the detected text regions. Understanding ResNet

BiLSTM: A sequence modeling approach that ensures the text is read in context, preserving both flow and coherence. Understanding BiLSTM

Attention: For making the final prediction, focus on the relevant portions of the text. This approach resulted in 99.58% accuracy for text recognition.

3. Preprocessing and Post-Processing Systems

Skewed or poorly scanned images are often a challenge in document recognition systems. Our system includes an advanced preprocessing pipeline that detects and corrects skewness, ensuring high-quality inputs for the recognition phase. Additionally, a custom post-processing module helps solve the issue of partial text detection, ensuring that no text is missed.

 

How It Works

 The system takes an image or PDF as input and processes it in two main stages:

Text Detection: Using YOLOv7, the system identifies text boxes and maps their coordinates.

Text Recognition: The TPS-ResNet-BiLSTM-Attention architecture processes these text regions, transforming them into structured, readable text.

The final output is a JSON file containing all the necessary information, including page structure, text box coordinates, and recognized text content.

Applications

• Invoice and Receipt Processing: Automates the extraction of financial details such as totals, dates, and vendor names.
• Form Digitization: Converts printed forms into structured digital formats.
• Book Archiving: Transforms printed books into searchable, editable digital files.

Why It Stands Out

This system stands out due to its high accuracy, flexibility, and advanced features. By using YOLOv7, it excels at detecting text in varied and challenging layouts, while the TPS-ResNet-BiLSTM-Attention architecture ensures that the text is recognized with minimal errors. With an overall accuracy of 98.56%, businesses can confidently automate document processing tasks without sacrificing precision.

Conclusion

This text recognition system represents a significant leap forward in document processing technology. Whether you need to automate data entry from forms and invoices or digitize entire books, this solution is designed to handle even the most complex challenges. Empower your business with next-generation text recognition today!