Jan 5
What is Optical Character Recognition(OCR)?
OCR (Optical Character Recognition, Optical Character Recognition) checks characters printed on paper with electronic devices, determines their shape by detecting dark and light patterns, and then uses character recognition methods to translate the forms into computer text. The text in a paper document is optically converted into a black and white dot-matrix image file for printed characters. The text in the image is transformed into a text format through the recognition software to edit further. The leading indicators to measure the performance of an OCR system are rejection rate, misrecognition rate, recognition speed, user-friendliness, product stability, ease of use and feasibility, etc.
How to Train an OCR Model
The model is required to provide the correct data. For example, if you are training a model to transcribe receipts automatically, your training data should include all the values you want to transcribe: name, amount, time, etc. In a word, the data should consist of the value you are looking for. In addition, the data should also be comprehensive, including images from different angles, different types of image quality, and so on.
Application of OCR
Retail: Retailers use serial numbers to describe their products. Robots can scan them from barcodes in retail stores or warehouses, using OCR technology for information extraction and inventory tracking.
Banking: Now, you can use your smartphone to take photos of the front and back of the check you want to deposit. AI-driven OCR technology can automatically examine the validity and confirm whether the check matches the amount you wish to deposit.
Customized Dataset
With the acceleration of the commercialization of AI and the application of AI technologies such as assisted driving and customer service chatbot in all walks of life, the expectation of data quality in the special scenarios is getting higher and higher. High-quality labeled data would be one of the core competitiveness of AI companies.
If the general datasets used by the previous algorithm model are coarse grains, what the algorithm model needs at present is a customized nutritious meal. If companies want to further improve certain models’ commercialization, they must gradually move forward from the general dataset to create the unique one.
OCR Image Collection Project Case Study
Customer demand: Indonesian signboard, tens of thousands of advertisement collection
Difficulty: Strict requirements for image size, pixels, and the proportion of the signboard
Regarding data collection, ByteBridge has abundant overseas resources, covering Asia, Southeast Asia, the Middle East, North America, South America, Europe, Africa, and other regions. Participants can be found in a short time.
Moreover, the ByteBridge data collection platform supports collection personnel to upload images by themselves, and the QA team can review them in real-time.
ByteBridge, a Human-powered and ML-powered Data Labeling Tooling SaaS Platform
ByteBridge, a human-powered and ML-powered data labeling tooling platform with real-time workflow management, providing high-quality data with efficiency.
Accuracy and Efficiency
- ML-assisted capacity can help reduce human errors by automatically pre-labeling
- The real-time QA and QC are integrated into the labeling workflow as the consensus mechanism is introduced to ensure accuracy
- Consensus — Assign the same task to several workers, and the correct answer is the one that comes back from the majority output
- All work results are completely screened and inspected by the machine and human workforce
In this way, ByteBridge can affirm our data acceptance and accuracy rate is over 98%
Communication Cost Saving
On ByteBridge’s SaaS dashboard, developers can start the labeling projects by using the labeling instruction template and get the results back instantly.
From online setting labeling briefing to expert support alongside, the instruction communication is not that hard anymore.
Configure Your Own Annotation Project
In addition, clients can iterate data features, attributes, and workflow, scale up or down, make changes based on what they are learning about the model’s performance in each step of test and validation.
As a fully managed platform, it enables developers to manage and monitor the overall data labeling process and provides API for data transfer. The platform also allows users to get involved in the QC process.
These labeling tools are already available on the dashboard: Image Classification, 2D Boxing, Polygon, Cuboid.
We can provide personalized annotation tools and services according to customer requirements.
Cost-effective
A collaboration of the human-work force and AI algorithms ensure a 50% lower price compared to the conventional market.
End
If you need data labeling and collection services, please have a look at bytebridge.io, the clear pricing is available.
Please feel free to contact us: support@bytebridge.io
