Ningbo, China

Chengchen Ma


Average Co-Inventor Count = 11.5

ph-index = 1

Forward Citations = 3(Granted Patents)


Company Filing History:


Years Active: 2012

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2 patents (USPTO):Explore Patents

Title: Chengchen Ma: Innovator in Handwritten Character Recognition

Introduction

Chengchen Ma is a notable inventor based in Ningbo, China. He has made significant contributions to the field of handwritten character recognition, particularly focusing on Arabic and Latin letters. With a total of 2 patents, his work showcases innovative methods that enhance the accuracy and efficiency of identifying handwritten characters.

Latest Patents

Chengchen Ma's latest patents include an "On-line identifying method of hand-written Arabic letter" and an "Identifying method of hand-written Latin letter." The first patent discloses a method that utilizes a multilayer coarse classification algorithm based on the local characteristics of Arabic letters. This approach allows for the effective matching of inputted handwritten Arabic letters with standard letters stored in a predetermined library. The second patent focuses on the identification of handwritten Latin letters, extracting stable characteristics from various handwritten styles. This method classifies Latin letters into smaller aggregations, improving the identification rate and speed.

Career Highlights

Chengchen Ma is currently employed at Ningbo Sunrun Electric & Information Science and Technology Development Co., Ltd. His work at this company has allowed him to further develop his innovative ideas and contribute to advancements in character recognition technology.

Collaborations

Chengchen has collaborated with several talented individuals, including Jiaming He and Jianfen Wen. These collaborations have fostered a creative environment that encourages the exchange of ideas and expertise.

Conclusion

Chengchen Ma's contributions to the field of handwritten character recognition demonstrate his commitment to innovation and technology. His patents reflect a deep understanding of the complexities involved in recognizing handwritten characters, paving the way for future advancements in this area.

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