In the pre-digital era, companies meticulously maintained their records in large folders and kept them on shelves in a specific logical order. Retrieving information from these records was time-consuming and required the expertise of several specialists.
However, with the advent of scanners, it became possible to digitize archives and simplify searching for information.
Now, imagine if you could make your work easier and optimize tasks related to searching or analyzing scanned archival information. This is where automatic text extraction from pictures comes into play. It can save you time and effort by quickly extracting relevant information from images.
Identity verification and digitization have become increasingly important due to the rise of information technology. Companies often ask for photos or scanned copies of documents for confirmation, but manual checks are slow, monotonous, costly, and error-prone.
However, automated processing can extract textual information from these images quickly and accurately, making the verification process much more manageable.
Figure 1. The pipeline of data extraction from the structured document
Advantages of Automated Image Text Extraction over Manual Input:
Speed: Machine learning tools can extract data faster without manual verification.
Accuracy: Reduces errors common in manual data entry.
Seamless Integration: It can be effortlessly incorporated into various applications and services.
Enhanced Security: Automation minimizes risks associated with manual handling of confidential data.
Approaches to Image Text Extraction:
Extracting text from images is a complex task, given the variety of existing documents. To handle this, multiple techniques have been developed, each with its own strengths and limitations. These techniques are suitable for specific contexts and types of images. Let's discuss the primary approaches used for image text extraction and explore their nuances:
Template-Based Approach:
Uses known layouts to identify the type of information.
Uses OCR (Optical Character Recognition) to extract the text.
Advantages: Low data requirements and reads only necessary info.
Drawbacks: Limited to known document types and needs a classifier.
Natural Language Processing (NLP) Based Approach:
Extracts all text and tags each segment using Named Entity Recognition (NER).
Advantages: No need to label fields and can handle diverse document structures.
Drawbacks: Challenges with areas classified into one class and requires language-specific training.
Graph Convolutional Networks:
Converts the document image into a graph, identifying relationships between data.
Advantages: Recognizes relationships, no need for template labeling.
Drawbacks: Requires substantial data and resources for training.
Figure 2. Graph convolution neural network workflow
Optical Character Recognition (OCR):
A sophisticated mechanism that transmutes images into readable text, using subprocesses like image preprocessing, text localization, and character recognition.
The study successfully utilized Tesseract, an open-source text recognition system.
Figure 3. OCR process flow
Processing Stages:
Extracting text from images isn't simply about retrieving textual content; it involves refining and preparing the image for extraction, followed by post-processing the obtained information. To better understand this comprehensive procedure, let's break down the essential stages involved in the processing of image-based texts:
1. Preprocessing: Includes steps like image registration (aligning the document photo with a reference template) and denoising techniques.
2. Text Post-processing: Cleans the extracted text based on field types and ensures the text conforms to expected formats.
Conclusion:
The image text extraction now reaches a new qualitative level and makes it possible to provide a fast, accurate, and secure way of data acquisition from images and scanned documents. This technology can be particularly beneficial for sectors like banking, government and corporate. By training optical character recognition engines like e.g. Tesseract on custom data and integrating additional instruments like graph convolution networks, there is a considerable potential to enhance accuracy and flexibility further.
